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Rank order-based recommendation approach for multiple featured products
Expert Systems with Applications 38 (2011) 7081–7087
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Rank order-based recommendation approach for multiple featured products Sang Hyun Choi a, Byeong Seok Ahn b,⇑ a b
Department of Industrial and System Engineering, Engineering Research Institute, Gyeongsang National University, 900 Gazwadong, Jinju, Gyeongnam 660-701, South Korea College of Business Administration, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, South Korea
a r t i c l e
i n f o
Keywords: Personalized recommendation Ordinal weight Similarity measure Multi-attribute value
a b s t r a c t Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Electronic Commerce (e-Commerce) provides a new gateway for customers shopping online. One of the most significant advantages offered by online shopping is convenience. Online shopping is no longer a time-consuming task; in contrast, it is an energy-saving activity. Therefore, shortening customers’ product searching time is the key to an online shop’s success. In order to serve customers instantly and efficiently, it is essential to recognize each customer’s unique and particular needs and recommend a personalized shopping list. Recommendation algorithms are best known for their use in eCommerce websites, where many researchers use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but others also utilize attributes like the items viewed, demographic data, subject interests, and favorite artists (Linden, Smith, & York, 2003). Because recommendations are based on these sources of data, the majority of product recommendation systems are developed with content-based, collaborative, constraint-based filtering or knowledge-based methods as their underlying technologies (Burke, 2000; Xiao, Aimeur, & Fernandez, 2003). Various schemes aimed at personalizing recommendations for customers may suffer because of early rating, sparsity, or scalability. Despite evidence that ⇑ Corresponding author. Tel.: +82 2 820 5582; fax: +82 2 821 6385. E-mail address: [email protected] (B.S. Ahn). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.12.062
customers usually consider multiple specifications of a purchasing product, most of prior researches on personalized recommendations have focused on customers’ overall ratings for each product. One technology commonly used to address information overload challenges is information filtering, which focuses on classifying streams of new content into categories. Information filtering systems require a profile of user needs or preferences. These techniques play a central role in recommender systems. They build a profile of user preferences that is particularly valuable when a user encounters new content that has not been rated before (Good et al., 1999). As reviewed by Adomavicius and Tuzhilin (2005), information filtering techniques have focused on the values of the features that are explicitly associated with the products or items that these techniques recommend. These techniques have not taken into account individual preferences about the degree of each feature’s importance. Instead, the techniques have used holistic evaluations across multiple features as input data. In this paper, we extend previous work by explicitly considering the multidimensional specifications that characterize each product. Multidimensional specifications, known as ‘multi-attribute weights’ in the context of multi-attribute decision analysis, are placed in the form of rank-order by the customer. There are a variety of situations where it is reasonable to only use rank-order information about attribute weights. The final objective of this paper is to present a new recommendation method that is different from previously-reported recommendation systems in that it explicitly takes into account the multidimensional features of each product by employing an ordered, weights-based, multi-attribute value (O-MAV) model. To
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evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. 2. Prior researches The explosive growth of the World Wide Web and the emergence of e-Commerce have led to the development of recommender systems. Recommender systems are personalized information filtering technologies used to (1) predict whether a particular user will like a particular item (prediction problem) or (2) identify a set of N items that will be of interest to a certain user or customer (top-N recommendation problem). In recent years, recommender systems have been used in a number of different applications, such as recommending products a customer will most likely buy, suggesting movies or music a customer will find enjoyable, identifying web-pages that will be of interest, or even proposing alternate ways of searching for information (Han & Karypis, 2005). Excellent overviews of various applications for recommender systems can be found in Adomavicius and Tuzhilin (2005) and Schafer, Konstan, and Riedl (1999). Over the years, various approaches for building recommender systems have been developed by utilizing demographic, contentbased, or historical information (Balabanovic & Shoham, 1997; Pazzani, 1999; Shardanand & Maes, 1995; Terveen, Hill, Amento, McDonald, & Creter, 1997). Among these approaches, collaborative filtering (CF), which relies on historical information, is probably the most successful and widely used technique for building recommender systems (Han & Karypis, 2005; Konstan et al., 1997). Despite its success, however, widespread use has exposed some well-known limitations; these include early rating, sparsity, and scalability, and they can lead to poor recommendations (Cho, Kim, & Kim, 2002; Sarwar, Karypis, Konstan, & Riedl, 2000). For commodities like computers or home theater systems, frequent purchases are seldom made by a specific customer. It seems difficult to infer a customer’s previous preferences on such products because (1) there may not be enough information about the customer’s past purchases and (2) the customer may have specific requirements for a single purchase. A new approach appears to handle these difficulties in acquiring input data by recommending products with a minimum level of information. This ‘minimum level of information’ refers to two kinds of input data: the specification weights that denote the relative importance of specifications and the specification values within which a customer wants to search for products. The majority of relevant recommendation methods using specification values and weights have been implemented into the automated recommendation system (Guttman, Moukas, & Maes, 1998). It is necessary to compare and analyze the algorithms embedded in recommender systems in order to explain our contribution. Our approach is a product brokering approach, which involves the retrieval of information to help the customer determine what to buy (Guttman et al., 1998). This approach encompasses the evaluation of product alternatives based on consumer-provided criteria. The result of this approach is the ‘‘consideration set’’ of products. Several recommender systems are based on the product brokering model, such
as PersonaLogic, Firefly, Jango, LDW, and FindMe systems (Burke, 2000; Doorenbos, Etzioni, & Weld, 1997; Guttman & Maes, 1999; Shardanand & Maes, 1995). As shown in Table 1, the recommender systems are arranged by types of weights, types of specification values, the degree of input burden, and underlying methodology. While the Firefly and the Jango do not consider any specification weights, the PersonaLogic and the LDW require them in the form of cardinal values and use them to calculate the aggregated value of each product. The FindMe uses a knowledge-based similarity retrieval algorithm. There are two kinds of retrieval modes: similarity finding and tweak application. With respect to the types of specification values and methodologies adopted, the PersonaLogic obtains values in the form of hard or soft constraints, filters products with the hard constraints, and selects products that best satisfy the soft constraints. The Firefly requires the holistic judgment of the user preference for each product in the form of a seven-level Likert scale and uses the CF technique for recommending products. The Jango asks the customer to enter the desired specification values and then suggests products that are closest to the desired level. The LDW obtains the customer’s preferences about specification values in the form of a utility, calculates a weighted sum of specification values for each product, and suggests the product with the largest value. As the recommender systems require more information from the customer, the degree of burden that the customer feels increases. The Jango imposes a low degree of burden on the customer, the PersonaLogic, the Firefly, and the FindMe impose a medium degree of burden, and the LDW imposes a high degree of burden. High degrees of burden arise because of the difficulty in obtaining the cardinal utility systematically from the customer. Recently, Choi and Cho (2004) developed a utility range-based product recommendation method in which they assume the additive utility model as an underlying model. They further assume that the customer provides specification weights in incomplete forms, but the specification values are expressed in numerical values. In the research area of MAV models, there have been research works on the less-specific expression of the multi-attribute weights. The researches on the rank-order weights also fall into that category. The rank-order centroid (ROC) weights, which represent the center of admissible weights of the rank-order, successfully reflect the user’s intention (Barron & Barret, 1996). Their claim has proved to be legitimate in a similar situation (Ahn & Park, 2008). A new recommendation method, the O-MAV model, differs from previously reported recommendation methods in that (1) the method does not need to obtain demographic or transactional information about the customers, (2) customers need only to specify the rank-order information that denotes the relative importance among the specifications, and (3) customers can generate, if they wish, specification ranges for some or all specifications to reduce searching efforts (in a database). We shall introduce the O-MAV method in the next section.
Table 1 Classification of the recommendation approaches. Recommender
Weight type
Value type
Input burden
Method
PersonaLogic Firefly Jango LDW FindMe
Cardinal None None Cardinal Filtering
Constraint value Seven Scale Specification value Utility Specification and Constraint value
Medium Medium Low High Medium
CSP CF Query MAUT/AHP Query and Similarity
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We next describe in detail an algorithm for recommending products.
3. The O-MAV model for personalized recommendations 3.1. General description
3.2. Computing the marginal value
n X
wi v i ðaj Þ;
ð1Þ
i¼1
where MAVj is the aggregated multi-attribute value of product j and P the weights ni¼1 wi ¼ 1 such that wi P 0, i = 1, . . ., n, reflect the contribution of the ith specification value to the overall value. The MAV model involves (1) an assessment of the customer’s preference values over the various specifications relevant to a recommendation and (2) an assessment of the weights for each of the specifications. In other words, the MAV approach requires that each customer provide a large number of precise indifference judgments to result in complete marginal value functions and weights. While formally elegant, this assessment procedure may be difficult and time-consuming to implement in practical recommendation systems. As stated by Weber (1987), there exists a gap between theoretical research and practical needs. This gap may arise because the preferences of the user were not sufficiently well structured to allow the successful application of such a decisionanalysis method. For instance, the user may not be willing or able to specify the preference in the detailed manner required by the methodology, or he may not provide exact estimations of the decision parameters that require a considerable cognitive burden (Ahn & Park, 2008). By allowing less-specific information representation, however, the burden of information specification imposed on the user can be relieved to some extent. Thus we can obtain a less specific expression that renders the user judgments that are readily available. The relaxation of such precise preference judgments is therefore advantageous and offers a method for reducing the gap between theoretical research and practical needs. In the proposed O-MAV model, a range value is allowed for some or all of the specifications; this range represents the values that the user wants to attain. For example, a customer wants to buy a notebook at the price of $1,200–$1,400. The importance of the specifications can also be specified in rank order. For example, the customer may think that design is the most, brand the next most, and price the least important when considering three specifications of price, design, and brand. The ranges are regarded as a prior search condition, which uses the concept of TQL (Tapestry Query Language) suggested by Goldberg, Nichols, Oki, and Terry (1992). Given a rank-order of weights and the ranges of the specifications, we use the MAV model in (1) to obtain the aggregated values of products and generate personalized recommendation products according to the magnitude of the aggregated values.
v i ¼ ðxi xi Þ=ðxþi xi Þ v i ¼ ðxi xþi Þ=ðxi xþi Þ
for a better value for a larger number
ð2aÞ
for a better value for a smaller number ð2bÞ
where, if a larger number indicates a higher preference, the specification is ‘better for a larger’ one. Otherwise, the specification is ‘better for a smaller’ one. The transformation process is graphically illustrated in Fig. 1, which considers two cases with and without prior range input. Fig. 1a shows the specification without prior range input; in this case, the customer does not express any preferences to limit the search of products in the database. In contrast, Fig. 1b shows the specification with range input; in this case, the customer expresses the range of specification value within which he wants to search for products. If the range is specified by þ ½x i ; xi for the ith specification, the values within this range are transformed via a positive linear transformation as shown in formuþ þ las (2a) and (2b) with end points v i ¼ v i ðxi Þ and v i ¼ v i ðxi Þ. For the specifications without prior range input, x corresponds to the i minimum value and xþ corresponds to the maximum value among i all possible values of the specification. 3.3. Computing weights As stated above, the proposed model assumes that the specification weights are rank-ordered. Without loss of generality, we assume that the attribute indices are coded such that the weights are in descending order (i.e., from the most important to the least important specification). Therefore, the set of weighting vectors compatible with this information is:
( W¼
wjw1 P w2 P . . . P wn P 0;
n X
) wi ¼ 1 :
ð3Þ
i¼1
Given a set W, several dominance rules are applicable to identify products with larger MAVs that seem to fulfill the customer’s preferences as nearly as possible. The concept of dominance is widely utilized to address this problem and can be realized in the following linear program for identifying paired dominance (PD) between different products k and j:
1
Preference Value
MAVj ¼
A normalization of specification values is performed to facilitate the computational problems arising from different units, and thus aims to generate comparable scales (Hwang & Yoon, 1980; Nefti, Oussalah, & Rezgui, 2009). In the proposed O-MAV model, unless otherwise stated, linear normalization is performed to obtain vi(aj) 2 [0, 1], i = 1, . . ., m, j = 1, . . ., n as follows:
Preference Value
Recent product recommendation algorithms have attempted to find interesting sets of products, not from a holistic evaluation across multiple features, but instead from individual evaluations of multiple features characterizing the products. Multi-attribute decision making (MADM) models can therefore be effectively utilized because they provide a method for evaluating decision alternatives with a finite number of features or attributes. A well-known method for the valuation of alternatives over multiple attributes involves using an additive form. In this method, the normalized values of alternatives that are measured with respect to each attribute are added to obtain the overall valuation of the alternative across multiple attributes. Suppose that there is a finite set of m products A = {a1, . . ., am}, each of which is evaluated by considering a set of n specifications X = {x1, . . ., xn}. Let vi(aj) 2 [0, 1] denote the value associated with the jth product with respect to the ith specification xi. The underlying evaluation model is an additive MAV model, and thus the overall value of an alternative aj 2 A is given by
1
0
0
min max Value of specification (a) Without range input
min max Value of specification (b) With range input
Fig. 1. Graphical illustration of the value transformation method.
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( ) n X PDkj ¼ min wi ½v i ðak Þ v i ðaj Þjw 2 W :
Table 2 The mobile phones characterized by four characteristics.
i¼1
If the optimal objective value PDkj P 0, then product k dominates j; otherwise, it does not. In the given w e W, PDkj is obtained when product k achieves the worst scenario and product j achieves the best scenario. While formally elegant and generally acceptable, this dominance approach frequently results in almost no prioritization of alternatives or too many non-dominated alternatives (Kirkwood & Corner, 1993). Using rank-based approximate weights offers a distinct approach for circumventing this problem (Stillwell, Seaver, & Edwards, 1981). It permits the determination of a best product and/or ranking product by developing a set of approximate weights from the given ranked weights for use with a MAV function. Several methods for selecting approximate weights, including equal weights and rank-order centroid (ROC) weights, have been proposed and evaluated (Ahn & Park, 2008; Barron & Barret, 1996). A common conclusion of these studies is that ROC weights, defined as the mean of the extreme points of ranked weights and denoted by n 1X 1 wi ¼ n m¼i m
for i ¼ 1; . . . ; n;
ð4Þ
have an appealing theoretical rationale and appear to perform more accurately than the other rank-based approximate schemes. Therefore, the ROC weights can be used to evaluate products and select the product with the largest MAV.
O-MAVkj ¼ arg max j
P
n X i¼1
n n X 1X 1 v i ðak Þ n m i¼1 m¼i
n 1X 1 v i ðaj Þ for k – j; j ¼ 1; . . . ; m n m¼i m
ð5Þ
This model recommends personalized products by using the customer’s minimal input data in the process of searching for products that the customer wants. To recommend products, we require only ordinal information about the weights and preferences of the specification values. An efficient way to obtain ordinal information involves asking the customer to enter preferences about the desirable range of the most important specification, then the next most important one, and so on. If the customer only provides the ranking of a few specifications near the top and has difficulty in specifying the ranking of the other specifications, we then calculate the weights with the ROC method using only the ranking of the specifications named by the customer; we equally assign any remaining weights to the rest of the specifications. For example, let us consider a customer who chooses only the two most important specifications among five. The weight of the specification chosen at first is calculated by (1/3)(1 + 1/2 + 1/3) = 11/18, that of the second specification by (1/3)(1/2 + 1/3) = 5/18, and that of the remaining three specifications by (1/3)(1 16/18) = 1/27. 3.4. Explanatory example To illustrate the O-MAV method, we consider the selection of one mobile phone among eight phones characterized by the four specifications in Table 2. The specifications of ‘‘maker’’, ‘‘phone type’’, and ‘‘memory size for MP3’’ are represented by categorical values. Each of these is later transformed into a numerical value in such a way that a larger value is attached to the preferred categorical value. In the case of the ‘‘maker’’ and ‘‘phone type’’ specifications, we assume that the degrees of preference are determined by the degrees of popularity in the market. Users are able to individually change the order of categorical values. The other specification is price, which is
*
Model name
Maker (w1)
Price (w2)
Phone type (w3)
Memory* (w4)
SV-590 SCH-B410 SCH-V740 SD-290 MS550 SCH-G100 SB-130 SCH-W200
LT/2 SM/3 SM/3 LT/2 KM/1 SM/3 LT/2 SM/3
228,660 320,000 258,720 222,420 320,460 294,000 294,000 299,000
Slim/2 Dual Folder/1 Slim/2 Slide/3 Slide/3 Slide/3 Slim/2 Slide/3
1 GB-E/5 120 MB-N/2 120 MB-E/3 60 MB-N/1 60 MB-N/1 60 MB-N/1 1 GB-N/4 120 MB-N/3
E has an extensible memory slot and N does not.
represented by Korean won. The price is better at smaller values, but the maker, phone type, and memory size for MP3 are better at larger values. Let us assume that the customer wants to select the most preferred phone subject to the following prior specification values:
(Condition (Condition (Condition (Condition
1) 2) 3) 4)
SM or LT for the maker, 200,000 won to 300,000 won for the price, slim or slide for the phone type, and more than 60 MB for the memory size.
Rank-order information among the specifications is obtained such that w1 P w2 P w3 P w4. This implies that the maker is at least as important as the price, the price is at least as important as the phone type, and the phone type is at least as important as the memory size. Applying the searching conditions excludes the SCH-B410 and MS550 from further consideration because the SCH-B410 does not satisfy conditions 2 and 3 and the MS550 does not satisfy conditions 1 and 2. Then, the problem is reduced to determining the best among six mobile phones (SV-590, SCHV740, SD-290, SCH-G100, SB-130, and SCH-W200). The marginal values of the six mobile phones can be obtained as shown in Table 3 by formula (2). The next step involves computing the specification weights using the rank-order of the specifications given by the customer. From the ROC method, the weight of the maker specification is 1/4(1 + 1/2 + 1/3 + 1/4) = 0.52, that of the price is 1/4(1/2 + 1/ 3 + 1/4) = 0.27, that of the phone type is 1/4(1/3 + 1/4) = 0.15, and that of the memory is 1/4(1/4) = 0.06. Finally, the aggregated values of the six mobile phones are computed by the weighted sum of the normalized values and specification weights:
v ðSV-590Þ ¼ ð0:52Þð0Þ þ ð0:27Þð0:92Þ þ ð0:15Þð0Þ þ ð0:06Þð1Þ ¼ 0:31;
v ðSCH-V740Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0:53Þ þ ð0:15Þð0Þ þ ð0:06Þð0:5Þ ¼ 0:69;
v ðSD-290Þ ¼ ð0:52Þð0Þ þ ð0:27Þð1Þ þ ð0:15Þð1Þ þ ð0:06Þð0Þ ¼ 0:42;
Table 3 The remaining mobile phones with transformed values. Model name
Maker (w1)
Price (w2)
Phone type (w3)
Memory (w4)
SV-590 SCH-V740 SD-290 SCH-G100 SB-130 SCH-W200
0 1 0 1 0 1
0.92 0.53 1 0.07 0.07 0
0 0 1 1 0 1
1 0.5 0 0 0.75 0.5
S.H. Choi, B.S. Ahn / Expert Systems with Applications 38 (2011) 7081–7087
v ðSCH-G100Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0:07Þ þ ð0:15Þð1Þ þ ð0:06Þð0Þ ¼ 0:69;
v ðSB-130Þ ¼ ð0:52Þð0Þ þ ð0:27Þð0:07Þ þ ð0:15Þð0Þ þ ð0:06Þð0:75Þ ¼ 0:06; and
v ðSCH-W200Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0Þ þ ð0:15Þð1Þ þ ð0:06Þð0:5Þ ¼ 0:7: Based on the computed values, we recommend the SCH-W200 to the customer since it has the largest MAV. Other mobile phones can also be chosen if the customer changes at least one of the preferences regarding the specification ranges and the weights. Remark: In the course of product recommendation, different recommendation methods can be applied to help the customer select the products that are likely to meet his or her needs. The Euclidean distance-based method is selected for comparison because it deals with multiple attribute values and is commonly used as a similarity measure in recommendation research areas (Schultz & Joachims, 2004). A classical recommendation method that uses the distance measure has to be modified to accommodate for the ranges of specifications. An ideal product can be thought of as one that displays the upper bounds for all of the specifications; in contrast, a poor product displays the lower bounds for all the specifications. As shown in (6a), recommending products based on the traditional distance measure involves selecting a product that shows the minimum distance from the ideal product. The traditional distance is found as follows:
TDj ¼ argminj TDj ¼ argminj
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 1 ð1 v ji Þ2 ; 8j; i
ri
ODj ¼ arg minj ODj ¼ arg minj
rX ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi wi ð1 v ji Þ2 ; 8j;
of user satisfaction. The experimental study was conducted in the following five steps. Step 1. Prepare the data set for the recommendation problem. The specific data for the mobile phones used in the experiment were gathered from a price comparison website in Korea. The mobile phones were evaluated with respect to six specifications: phone type, brand, MP3 memory size, type of DMB (digital multimedia broadcasting), pixel size of the digital camera, and price. Step 2. Enter the searching conditions. The participants entered the specification ranges within which they wanted to purchase the mobile phones from the most important specification to the least important one. Meanwhile, a rank-order of specifications was gathered. The mobile phones within the specification ranges were selected. Step 3. Compute the aggregated value for each of the selected mobile phones. The system computed the ROC weights according to the rank-order in step 2 and then calculated the MAVs and the two distance values based on the distance measures of the selected mobile phones. Step 4. Recommend the preferred mobile phone. The system suggested k most preferred mobile phones according to either (1) the magnitude of the aggregated values for the O-MAV model or (2) the smaller distances for the TD and OD models. Step 5. Evaluate the performance of each method. The participant entered the rank of his or her own phone in terms of the list of k mobile phones if it was in the list and N/A otherwise. The participants were also encouraged to enter the level of user satisfaction with each method.
ð6aÞ
where the relative weight for the ith specification is 1/rI, which is a standard deviation of the ith specification values. In this paper, we suggest a new way for determining similarity between products. Our method takes into account the personalized ordinal weights of specifications as follows:
Ordinal weighted distance :
7085
ð6bÞ
i
where wi, i = 1, . . ., n, is the ROC weight. The typical values of the weights are computed from the reciprocal of the standard deviations of the traditional distance measure (Shyu, Haruechaiyasak, & Chen, 2003). For the ordinal weighted distance, they are computed by the ROC method for comparative analysis with the O-MAV method. 4. Experimental evaluation 4.1. Experimental design An experimental study was conducted to compare the performance of three recommendation methods: the traditional distance-based method (TD), the ordinal weight-based distance method (OD), and the ordinal MAV (O-MAV) method. A total of 125 mobile phones offered by a leading Korean code division multiple access (CDMA) carrier, the S Telecom Company, were used for the experiment. The participants in the experiment were 60 students who had purchased mobile phones from the company during the last year (June 2005–June 2006). For the performance evaluation of the recommendation methods, the quality of the decisions resulting from each method was assessed by the accuracy and level
At step 2, the participants did not need to enter all of the specification ranges since this could be considered burdensome. Instead, we asked them to specify at least the two most important specifications in ranges for prior search conditions. To evaluate the quality of the three recommendation methods, we used the performance metrics of accuracy and satisfaction rate. We asked the students to enter information about the specification values of at least the two most important specifications that they considered when they bought their phones. They entered information in the order of the specification’s importance. After the system found the mobile phones that satisfied the students’ preferences as closely as possible, each user was encouraged to enter the rank of his/her own phone among the k mobile phones recommended by the system. Thus, the measure of accuracy was defined as the cumulative hitting ratio of each rank by each method. A higher hit ratio for top ranks in the list indicates that the method suggested the mobile phones that the participants were likely to purchase. We compared the results of each method. For the satisfaction rate, participants were asked to directly enter evaluation scores using a 10-point Likert scale (1 is very inaccurate and 10 is very accurate) after they examined the k recommended phones by accessing the linked product information sites. 4.2. Experimental results We analyzed the experimental results by using statistical methods to compare the average rank and satisfaction rate for the TD, OD, and O-MAV models. We obtained evaluation results from 54 of the 60 students because six students responded that they were unable to find their phones among the recommended products. These phones were not in the list of the 125 phones because the company had stopped marketing them. We therefore excluded these subjects from our analysis of experimental results. For the accuracy measure, the ratio that the students’ own phones were
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5. Concluding remarks
1
TD OD O-MAV
0.9 0.8
Accuracy
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
2
3
4
5
6
7
Rank of Product Recommended Fig. 2. Cumulative accuracy of the three methods.
the same as in the first rank among the k most recommended phones was 29.6% by TD, 48.1% by OD, and 51.9% by the O-MAV model. As shown in Fig. 2, we obtained 100% accuracy after we presented seven and five mobile phones to the participants by TD and OD, respectively. In contrast, the O-MAV model required only four mobile phones. This implies that the O-MAV model captured the participants’ purchasing needs at a lower number of mobile phones. We conducted the Mann–Whitney test for the three paired samples in order to statistically verify the accuracy and satisfaction rates of the three recommendation methods. The values in Table 4 show that the accuracy and satisfaction rates were statistically valid within a significance level of 0.05; the differences between the TD and OD models and between the TD and O-MAV models were thus present. The differences between the two metrics of the OD and O-MAV models were not statistically valid within a significance level of 0.05. We conclude that the methods using ROC weights may provide better performances than the TD in terms of accuracy and satisfaction rate; however, a significant difference between the OD and OMAV models was not found. Methods taking personalized weights into account provided greater accuracy and higher satisfaction rates than others because the O-MAV and OD models were designed to incorporate individual preferences through interviews with participants. In terms of ease of use, the O-MAV model allows customers to provide only rank-order weights; it thus reduces the burden of providing preference information about each product. In particular, 20 of 54 participants entered the range values about 2–4 among the six specifications and expressed that they were satisfied with the results. The O-MAV model is substantially better in terms of computational complexity; the distance-based methods must identify the distances through n(n 1)/2 computations, whereas the O-MAV model only needs to identify n aggregated values. Table 4 Difference test for the accuracy and satisfaction rates of the three methods. MW test
TD OD TD O-MAV OD O-MAV *
Accuracy
Satisfaction rate
Median
Significance level
Median
Significance level
2.0 1.0 2.0 2.0 1.0 2.0
0.0185*
7.0 9.0 7.0 8.5 9.0 8.5
0.0019*
0.0381* 0.7610
Significant at a confidence level of 99.0.
0.0013* 0.9583
The ever increasing online shopping market requires companies to offer personalized recommendations to help customers easily find the products they wish to purchase. We proposed a new recommendation method that attempts to suggest products from simple information, such as ordinal specification weights and specification values. These considerations lead to the ordinal weight-based distance and O-MAV models. In particular, the O-MAV model encourages customers to provide ranges about a subset of specifications; in doing so, it acquires a rank-order of specification weights. We also conducted experimental comparisons between the ordinal weight-based methods and the traditional distance-based method. As a result, these tests consistently show that the proposed method helps the participants select the very products that they want to purchase. Furthermore, participants using our proposed method expressed a high level of satisfaction compared to previously reported recommendation methods. However, this experiment was conducted for mobile phone selection for a limited number of participants. Therefore, further research encompassing other types of product selection problems and participants with diverse demographical properties should be conducted. Acknowledgements This research of the first author was financially supported by the Ministry of Education, Science Technology (MEST) and Korea Institute for Advancement of Technology (KIAT) through the Human Resource Training Project for Regional Innovation. The second author is deeply indebted to Y.S. Kwak for her kind and helpful assistance. References Adomavicius, G., & Tuzhilin, A. (2005). Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734–749. Ahn, B. S., & Park, K. S. (2008). Comparing methods for multiattribute decision making with ordinal weights. Computers and Operations Research, 35, 1660–1670. Balabanovic, M., & Shoham, Y. (1997). FAB: Content-based collaborative recommendation. Communications of the ACM, 40, 66–72. Barron, F. H., & Barret, B. E. (1996). Decision quality using ranked attribute weights. Management Science, 42, 1515–1523. Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Science, 69(Suppl. 32), 1–23. Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23, 329–342. Choi, S. H., & Cho, Y. H. (2004). A utility range-based similar product recommendation algorithm for collaborative companies. Expert Systems with Applications, 27, 549–557. Doorenbos, R., Etzioni, O., & Weld, D. (1997). A scalable comparison-shopping agent for the World Wide Web. In Proceedings of the first international conference on autonomous agents (Agents’97.) (pp. 39–48). Marina del Rey, CA: SIGGRAPH/ SIGART/AIGCHI. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35, 61–70. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J. L., et al. (1999). Combining collaborative filtering with personal agents for better recommendations. In Proc. Conf. Am. Assoc. artificial intelligence (AAAI-99) (pp. 439–446). American Association for Artificial Intelligence. Guttman, R. H., & Maes, P. (1999). Agent-mediated integrative negotiation for retail electronic commerce. Lecture Notes in Computer Science, 1571, 70–90. Guttman, R. H., Moukas, A. G., & Maes, P. (1998). Agent-mediated electronic commerce: A survey. The Knowledge Engineering Review, 13, 147–159. Han, E. H., & Karypis, G. (2005). Feature-based recommendation system. In Proceedings of the 14th ACM international conference on information and knowledge management (pp. 446–452). Bremen, Germany: ACM/SIGIR. Hwang, C. L., & Yoon, K. S. (1980). Multiple attribute decision making methods and applications: A state of art survey. New York: Springer-Verlag. Kirkwood, C. W., & Corner, J. L. (1993). The effectiveness of partial information about attribute weights for ranking alternatives in multiattribute decision making. Organization Behavior and Human Decision Processes, 54, 456–476.
S.H. Choi, B.S. Ahn / Expert Systems with Applications 38 (2011) 7081–7087 Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., & Riedl, J. (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40, 77–87. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-toitem collaborative filtering. IEEE Internet Computing, 7, 76–80. Nefti, S., Oussalah, M., & Rezgui, Y. (2009). A modified fuzzy clustering for documents retrieval: Application to document categorization. Journal of the Operational Research Society, 60, 384–394. Pazzani, M. (1999). A framework for collaborative, content-based, and demographic filtering. Artificial Intelligence Review, 13, 393–408. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In Proceedings of ACM Ecommerce conference (pp. 158–167). ACM Special Interest Group on Electronic Commerce. Schafer, J., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. In Proceedings of ACM E-commerce (pp. 158–166). ACM Special Interest Group on Electronic Commerce. Schultz, M., & Joachims, T. (2004). Learning a distance metric from relative comparison. Advances in Neural Information Processing Systems, 16, 41–48.
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Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating ‘word of mouth’. In Proceedings of the ACM computer–human interaction conference (CHI’95) (pp. 210–217). Denver: ACM Special Interest Group on Computer–Human Interaction. Shyu, M. M., Haruechaiyasak, C., & Chen, S. C. (2003). Category cluster discovery from distributed WWW directories. Information Sciences, 155, 181–197. Stillwell, W. G., Seaver, D. A., & Edwards, W. (1981). A comparison of weight approximation techniques in multiattribute utility decision making. Organization Behavior and Human Decision Processes, 28, 62–77. Terveen, L., Hill, W., Amento, B., McDonald, D., & Creter, J. (1997). PHOAKS: A system for sharing recommendations. Communications of the ACM, 40, 59–62. Weber, M. (1987). Decision making with incomplete information. European Journal of Operational Researches, 28, 44–57. Xiao, B., Aimeur, E., & Fernandez, J. M. (2003). PCFinder: An intelligent product recommendation agent for e-commerce. In Proceedings of IEEE international conference on E-commerce (pp. 181–188). IEEE Computer Society.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Rank order-based recommendation approach for multiple featured products Sang Hyun Choi a, Byeong Seok Ahn b,⇑ a b
Department of Industrial and System Engineering, Engineering Research Institute, Gyeongsang National University, 900 Gazwadong, Jinju, Gyeongnam 660-701, South Korea College of Business Administration, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, South Korea
a r t i c l e
i n f o
Keywords: Personalized recommendation Ordinal weight Similarity measure Multi-attribute value
a b s t r a c t Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Electronic Commerce (e-Commerce) provides a new gateway for customers shopping online. One of the most significant advantages offered by online shopping is convenience. Online shopping is no longer a time-consuming task; in contrast, it is an energy-saving activity. Therefore, shortening customers’ product searching time is the key to an online shop’s success. In order to serve customers instantly and efficiently, it is essential to recognize each customer’s unique and particular needs and recommend a personalized shopping list. Recommendation algorithms are best known for their use in eCommerce websites, where many researchers use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but others also utilize attributes like the items viewed, demographic data, subject interests, and favorite artists (Linden, Smith, & York, 2003). Because recommendations are based on these sources of data, the majority of product recommendation systems are developed with content-based, collaborative, constraint-based filtering or knowledge-based methods as their underlying technologies (Burke, 2000; Xiao, Aimeur, & Fernandez, 2003). Various schemes aimed at personalizing recommendations for customers may suffer because of early rating, sparsity, or scalability. Despite evidence that ⇑ Corresponding author. Tel.: +82 2 820 5582; fax: +82 2 821 6385. E-mail address: [email protected] (B.S. Ahn). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.12.062
customers usually consider multiple specifications of a purchasing product, most of prior researches on personalized recommendations have focused on customers’ overall ratings for each product. One technology commonly used to address information overload challenges is information filtering, which focuses on classifying streams of new content into categories. Information filtering systems require a profile of user needs or preferences. These techniques play a central role in recommender systems. They build a profile of user preferences that is particularly valuable when a user encounters new content that has not been rated before (Good et al., 1999). As reviewed by Adomavicius and Tuzhilin (2005), information filtering techniques have focused on the values of the features that are explicitly associated with the products or items that these techniques recommend. These techniques have not taken into account individual preferences about the degree of each feature’s importance. Instead, the techniques have used holistic evaluations across multiple features as input data. In this paper, we extend previous work by explicitly considering the multidimensional specifications that characterize each product. Multidimensional specifications, known as ‘multi-attribute weights’ in the context of multi-attribute decision analysis, are placed in the form of rank-order by the customer. There are a variety of situations where it is reasonable to only use rank-order information about attribute weights. The final objective of this paper is to present a new recommendation method that is different from previously-reported recommendation systems in that it explicitly takes into account the multidimensional features of each product by employing an ordered, weights-based, multi-attribute value (O-MAV) model. To
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evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. 2. Prior researches The explosive growth of the World Wide Web and the emergence of e-Commerce have led to the development of recommender systems. Recommender systems are personalized information filtering technologies used to (1) predict whether a particular user will like a particular item (prediction problem) or (2) identify a set of N items that will be of interest to a certain user or customer (top-N recommendation problem). In recent years, recommender systems have been used in a number of different applications, such as recommending products a customer will most likely buy, suggesting movies or music a customer will find enjoyable, identifying web-pages that will be of interest, or even proposing alternate ways of searching for information (Han & Karypis, 2005). Excellent overviews of various applications for recommender systems can be found in Adomavicius and Tuzhilin (2005) and Schafer, Konstan, and Riedl (1999). Over the years, various approaches for building recommender systems have been developed by utilizing demographic, contentbased, or historical information (Balabanovic & Shoham, 1997; Pazzani, 1999; Shardanand & Maes, 1995; Terveen, Hill, Amento, McDonald, & Creter, 1997). Among these approaches, collaborative filtering (CF), which relies on historical information, is probably the most successful and widely used technique for building recommender systems (Han & Karypis, 2005; Konstan et al., 1997). Despite its success, however, widespread use has exposed some well-known limitations; these include early rating, sparsity, and scalability, and they can lead to poor recommendations (Cho, Kim, & Kim, 2002; Sarwar, Karypis, Konstan, & Riedl, 2000). For commodities like computers or home theater systems, frequent purchases are seldom made by a specific customer. It seems difficult to infer a customer’s previous preferences on such products because (1) there may not be enough information about the customer’s past purchases and (2) the customer may have specific requirements for a single purchase. A new approach appears to handle these difficulties in acquiring input data by recommending products with a minimum level of information. This ‘minimum level of information’ refers to two kinds of input data: the specification weights that denote the relative importance of specifications and the specification values within which a customer wants to search for products. The majority of relevant recommendation methods using specification values and weights have been implemented into the automated recommendation system (Guttman, Moukas, & Maes, 1998). It is necessary to compare and analyze the algorithms embedded in recommender systems in order to explain our contribution. Our approach is a product brokering approach, which involves the retrieval of information to help the customer determine what to buy (Guttman et al., 1998). This approach encompasses the evaluation of product alternatives based on consumer-provided criteria. The result of this approach is the ‘‘consideration set’’ of products. Several recommender systems are based on the product brokering model, such
as PersonaLogic, Firefly, Jango, LDW, and FindMe systems (Burke, 2000; Doorenbos, Etzioni, & Weld, 1997; Guttman & Maes, 1999; Shardanand & Maes, 1995). As shown in Table 1, the recommender systems are arranged by types of weights, types of specification values, the degree of input burden, and underlying methodology. While the Firefly and the Jango do not consider any specification weights, the PersonaLogic and the LDW require them in the form of cardinal values and use them to calculate the aggregated value of each product. The FindMe uses a knowledge-based similarity retrieval algorithm. There are two kinds of retrieval modes: similarity finding and tweak application. With respect to the types of specification values and methodologies adopted, the PersonaLogic obtains values in the form of hard or soft constraints, filters products with the hard constraints, and selects products that best satisfy the soft constraints. The Firefly requires the holistic judgment of the user preference for each product in the form of a seven-level Likert scale and uses the CF technique for recommending products. The Jango asks the customer to enter the desired specification values and then suggests products that are closest to the desired level. The LDW obtains the customer’s preferences about specification values in the form of a utility, calculates a weighted sum of specification values for each product, and suggests the product with the largest value. As the recommender systems require more information from the customer, the degree of burden that the customer feels increases. The Jango imposes a low degree of burden on the customer, the PersonaLogic, the Firefly, and the FindMe impose a medium degree of burden, and the LDW imposes a high degree of burden. High degrees of burden arise because of the difficulty in obtaining the cardinal utility systematically from the customer. Recently, Choi and Cho (2004) developed a utility range-based product recommendation method in which they assume the additive utility model as an underlying model. They further assume that the customer provides specification weights in incomplete forms, but the specification values are expressed in numerical values. In the research area of MAV models, there have been research works on the less-specific expression of the multi-attribute weights. The researches on the rank-order weights also fall into that category. The rank-order centroid (ROC) weights, which represent the center of admissible weights of the rank-order, successfully reflect the user’s intention (Barron & Barret, 1996). Their claim has proved to be legitimate in a similar situation (Ahn & Park, 2008). A new recommendation method, the O-MAV model, differs from previously reported recommendation methods in that (1) the method does not need to obtain demographic or transactional information about the customers, (2) customers need only to specify the rank-order information that denotes the relative importance among the specifications, and (3) customers can generate, if they wish, specification ranges for some or all specifications to reduce searching efforts (in a database). We shall introduce the O-MAV method in the next section.
Table 1 Classification of the recommendation approaches. Recommender
Weight type
Value type
Input burden
Method
PersonaLogic Firefly Jango LDW FindMe
Cardinal None None Cardinal Filtering
Constraint value Seven Scale Specification value Utility Specification and Constraint value
Medium Medium Low High Medium
CSP CF Query MAUT/AHP Query and Similarity
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We next describe in detail an algorithm for recommending products.
3. The O-MAV model for personalized recommendations 3.1. General description
3.2. Computing the marginal value
n X
wi v i ðaj Þ;
ð1Þ
i¼1
where MAVj is the aggregated multi-attribute value of product j and P the weights ni¼1 wi ¼ 1 such that wi P 0, i = 1, . . ., n, reflect the contribution of the ith specification value to the overall value. The MAV model involves (1) an assessment of the customer’s preference values over the various specifications relevant to a recommendation and (2) an assessment of the weights for each of the specifications. In other words, the MAV approach requires that each customer provide a large number of precise indifference judgments to result in complete marginal value functions and weights. While formally elegant, this assessment procedure may be difficult and time-consuming to implement in practical recommendation systems. As stated by Weber (1987), there exists a gap between theoretical research and practical needs. This gap may arise because the preferences of the user were not sufficiently well structured to allow the successful application of such a decisionanalysis method. For instance, the user may not be willing or able to specify the preference in the detailed manner required by the methodology, or he may not provide exact estimations of the decision parameters that require a considerable cognitive burden (Ahn & Park, 2008). By allowing less-specific information representation, however, the burden of information specification imposed on the user can be relieved to some extent. Thus we can obtain a less specific expression that renders the user judgments that are readily available. The relaxation of such precise preference judgments is therefore advantageous and offers a method for reducing the gap between theoretical research and practical needs. In the proposed O-MAV model, a range value is allowed for some or all of the specifications; this range represents the values that the user wants to attain. For example, a customer wants to buy a notebook at the price of $1,200–$1,400. The importance of the specifications can also be specified in rank order. For example, the customer may think that design is the most, brand the next most, and price the least important when considering three specifications of price, design, and brand. The ranges are regarded as a prior search condition, which uses the concept of TQL (Tapestry Query Language) suggested by Goldberg, Nichols, Oki, and Terry (1992). Given a rank-order of weights and the ranges of the specifications, we use the MAV model in (1) to obtain the aggregated values of products and generate personalized recommendation products according to the magnitude of the aggregated values.
v i ¼ ðxi xi Þ=ðxþi xi Þ v i ¼ ðxi xþi Þ=ðxi xþi Þ
for a better value for a larger number
ð2aÞ
for a better value for a smaller number ð2bÞ
where, if a larger number indicates a higher preference, the specification is ‘better for a larger’ one. Otherwise, the specification is ‘better for a smaller’ one. The transformation process is graphically illustrated in Fig. 1, which considers two cases with and without prior range input. Fig. 1a shows the specification without prior range input; in this case, the customer does not express any preferences to limit the search of products in the database. In contrast, Fig. 1b shows the specification with range input; in this case, the customer expresses the range of specification value within which he wants to search for products. If the range is specified by þ ½x i ; xi for the ith specification, the values within this range are transformed via a positive linear transformation as shown in formuþ þ las (2a) and (2b) with end points v i ¼ v i ðxi Þ and v i ¼ v i ðxi Þ. For the specifications without prior range input, x corresponds to the i minimum value and xþ corresponds to the maximum value among i all possible values of the specification. 3.3. Computing weights As stated above, the proposed model assumes that the specification weights are rank-ordered. Without loss of generality, we assume that the attribute indices are coded such that the weights are in descending order (i.e., from the most important to the least important specification). Therefore, the set of weighting vectors compatible with this information is:
( W¼
wjw1 P w2 P . . . P wn P 0;
n X
) wi ¼ 1 :
ð3Þ
i¼1
Given a set W, several dominance rules are applicable to identify products with larger MAVs that seem to fulfill the customer’s preferences as nearly as possible. The concept of dominance is widely utilized to address this problem and can be realized in the following linear program for identifying paired dominance (PD) between different products k and j:
1
Preference Value
MAVj ¼
A normalization of specification values is performed to facilitate the computational problems arising from different units, and thus aims to generate comparable scales (Hwang & Yoon, 1980; Nefti, Oussalah, & Rezgui, 2009). In the proposed O-MAV model, unless otherwise stated, linear normalization is performed to obtain vi(aj) 2 [0, 1], i = 1, . . ., m, j = 1, . . ., n as follows:
Preference Value
Recent product recommendation algorithms have attempted to find interesting sets of products, not from a holistic evaluation across multiple features, but instead from individual evaluations of multiple features characterizing the products. Multi-attribute decision making (MADM) models can therefore be effectively utilized because they provide a method for evaluating decision alternatives with a finite number of features or attributes. A well-known method for the valuation of alternatives over multiple attributes involves using an additive form. In this method, the normalized values of alternatives that are measured with respect to each attribute are added to obtain the overall valuation of the alternative across multiple attributes. Suppose that there is a finite set of m products A = {a1, . . ., am}, each of which is evaluated by considering a set of n specifications X = {x1, . . ., xn}. Let vi(aj) 2 [0, 1] denote the value associated with the jth product with respect to the ith specification xi. The underlying evaluation model is an additive MAV model, and thus the overall value of an alternative aj 2 A is given by
1
0
0
min max Value of specification (a) Without range input
min max Value of specification (b) With range input
Fig. 1. Graphical illustration of the value transformation method.
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( ) n X PDkj ¼ min wi ½v i ðak Þ v i ðaj Þjw 2 W :
Table 2 The mobile phones characterized by four characteristics.
i¼1
If the optimal objective value PDkj P 0, then product k dominates j; otherwise, it does not. In the given w e W, PDkj is obtained when product k achieves the worst scenario and product j achieves the best scenario. While formally elegant and generally acceptable, this dominance approach frequently results in almost no prioritization of alternatives or too many non-dominated alternatives (Kirkwood & Corner, 1993). Using rank-based approximate weights offers a distinct approach for circumventing this problem (Stillwell, Seaver, & Edwards, 1981). It permits the determination of a best product and/or ranking product by developing a set of approximate weights from the given ranked weights for use with a MAV function. Several methods for selecting approximate weights, including equal weights and rank-order centroid (ROC) weights, have been proposed and evaluated (Ahn & Park, 2008; Barron & Barret, 1996). A common conclusion of these studies is that ROC weights, defined as the mean of the extreme points of ranked weights and denoted by n 1X 1 wi ¼ n m¼i m
for i ¼ 1; . . . ; n;
ð4Þ
have an appealing theoretical rationale and appear to perform more accurately than the other rank-based approximate schemes. Therefore, the ROC weights can be used to evaluate products and select the product with the largest MAV.
O-MAVkj ¼ arg max j
P
n X i¼1
n n X 1X 1 v i ðak Þ n m i¼1 m¼i
n 1X 1 v i ðaj Þ for k – j; j ¼ 1; . . . ; m n m¼i m
ð5Þ
This model recommends personalized products by using the customer’s minimal input data in the process of searching for products that the customer wants. To recommend products, we require only ordinal information about the weights and preferences of the specification values. An efficient way to obtain ordinal information involves asking the customer to enter preferences about the desirable range of the most important specification, then the next most important one, and so on. If the customer only provides the ranking of a few specifications near the top and has difficulty in specifying the ranking of the other specifications, we then calculate the weights with the ROC method using only the ranking of the specifications named by the customer; we equally assign any remaining weights to the rest of the specifications. For example, let us consider a customer who chooses only the two most important specifications among five. The weight of the specification chosen at first is calculated by (1/3)(1 + 1/2 + 1/3) = 11/18, that of the second specification by (1/3)(1/2 + 1/3) = 5/18, and that of the remaining three specifications by (1/3)(1 16/18) = 1/27. 3.4. Explanatory example To illustrate the O-MAV method, we consider the selection of one mobile phone among eight phones characterized by the four specifications in Table 2. The specifications of ‘‘maker’’, ‘‘phone type’’, and ‘‘memory size for MP3’’ are represented by categorical values. Each of these is later transformed into a numerical value in such a way that a larger value is attached to the preferred categorical value. In the case of the ‘‘maker’’ and ‘‘phone type’’ specifications, we assume that the degrees of preference are determined by the degrees of popularity in the market. Users are able to individually change the order of categorical values. The other specification is price, which is
*
Model name
Maker (w1)
Price (w2)
Phone type (w3)
Memory* (w4)
SV-590 SCH-B410 SCH-V740 SD-290 MS550 SCH-G100 SB-130 SCH-W200
LT/2 SM/3 SM/3 LT/2 KM/1 SM/3 LT/2 SM/3
228,660 320,000 258,720 222,420 320,460 294,000 294,000 299,000
Slim/2 Dual Folder/1 Slim/2 Slide/3 Slide/3 Slide/3 Slim/2 Slide/3
1 GB-E/5 120 MB-N/2 120 MB-E/3 60 MB-N/1 60 MB-N/1 60 MB-N/1 1 GB-N/4 120 MB-N/3
E has an extensible memory slot and N does not.
represented by Korean won. The price is better at smaller values, but the maker, phone type, and memory size for MP3 are better at larger values. Let us assume that the customer wants to select the most preferred phone subject to the following prior specification values:
(Condition (Condition (Condition (Condition
1) 2) 3) 4)
SM or LT for the maker, 200,000 won to 300,000 won for the price, slim or slide for the phone type, and more than 60 MB for the memory size.
Rank-order information among the specifications is obtained such that w1 P w2 P w3 P w4. This implies that the maker is at least as important as the price, the price is at least as important as the phone type, and the phone type is at least as important as the memory size. Applying the searching conditions excludes the SCH-B410 and MS550 from further consideration because the SCH-B410 does not satisfy conditions 2 and 3 and the MS550 does not satisfy conditions 1 and 2. Then, the problem is reduced to determining the best among six mobile phones (SV-590, SCHV740, SD-290, SCH-G100, SB-130, and SCH-W200). The marginal values of the six mobile phones can be obtained as shown in Table 3 by formula (2). The next step involves computing the specification weights using the rank-order of the specifications given by the customer. From the ROC method, the weight of the maker specification is 1/4(1 + 1/2 + 1/3 + 1/4) = 0.52, that of the price is 1/4(1/2 + 1/ 3 + 1/4) = 0.27, that of the phone type is 1/4(1/3 + 1/4) = 0.15, and that of the memory is 1/4(1/4) = 0.06. Finally, the aggregated values of the six mobile phones are computed by the weighted sum of the normalized values and specification weights:
v ðSV-590Þ ¼ ð0:52Þð0Þ þ ð0:27Þð0:92Þ þ ð0:15Þð0Þ þ ð0:06Þð1Þ ¼ 0:31;
v ðSCH-V740Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0:53Þ þ ð0:15Þð0Þ þ ð0:06Þð0:5Þ ¼ 0:69;
v ðSD-290Þ ¼ ð0:52Þð0Þ þ ð0:27Þð1Þ þ ð0:15Þð1Þ þ ð0:06Þð0Þ ¼ 0:42;
Table 3 The remaining mobile phones with transformed values. Model name
Maker (w1)
Price (w2)
Phone type (w3)
Memory (w4)
SV-590 SCH-V740 SD-290 SCH-G100 SB-130 SCH-W200
0 1 0 1 0 1
0.92 0.53 1 0.07 0.07 0
0 0 1 1 0 1
1 0.5 0 0 0.75 0.5
S.H. Choi, B.S. Ahn / Expert Systems with Applications 38 (2011) 7081–7087
v ðSCH-G100Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0:07Þ þ ð0:15Þð1Þ þ ð0:06Þð0Þ ¼ 0:69;
v ðSB-130Þ ¼ ð0:52Þð0Þ þ ð0:27Þð0:07Þ þ ð0:15Þð0Þ þ ð0:06Þð0:75Þ ¼ 0:06; and
v ðSCH-W200Þ ¼ ð0:52Þð1Þ þ ð0:27Þð0Þ þ ð0:15Þð1Þ þ ð0:06Þð0:5Þ ¼ 0:7: Based on the computed values, we recommend the SCH-W200 to the customer since it has the largest MAV. Other mobile phones can also be chosen if the customer changes at least one of the preferences regarding the specification ranges and the weights. Remark: In the course of product recommendation, different recommendation methods can be applied to help the customer select the products that are likely to meet his or her needs. The Euclidean distance-based method is selected for comparison because it deals with multiple attribute values and is commonly used as a similarity measure in recommendation research areas (Schultz & Joachims, 2004). A classical recommendation method that uses the distance measure has to be modified to accommodate for the ranges of specifications. An ideal product can be thought of as one that displays the upper bounds for all of the specifications; in contrast, a poor product displays the lower bounds for all the specifications. As shown in (6a), recommending products based on the traditional distance measure involves selecting a product that shows the minimum distance from the ideal product. The traditional distance is found as follows:
TDj ¼ argminj TDj ¼ argminj
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 1 ð1 v ji Þ2 ; 8j; i
ri
ODj ¼ arg minj ODj ¼ arg minj
rX ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi wi ð1 v ji Þ2 ; 8j;
of user satisfaction. The experimental study was conducted in the following five steps. Step 1. Prepare the data set for the recommendation problem. The specific data for the mobile phones used in the experiment were gathered from a price comparison website in Korea. The mobile phones were evaluated with respect to six specifications: phone type, brand, MP3 memory size, type of DMB (digital multimedia broadcasting), pixel size of the digital camera, and price. Step 2. Enter the searching conditions. The participants entered the specification ranges within which they wanted to purchase the mobile phones from the most important specification to the least important one. Meanwhile, a rank-order of specifications was gathered. The mobile phones within the specification ranges were selected. Step 3. Compute the aggregated value for each of the selected mobile phones. The system computed the ROC weights according to the rank-order in step 2 and then calculated the MAVs and the two distance values based on the distance measures of the selected mobile phones. Step 4. Recommend the preferred mobile phone. The system suggested k most preferred mobile phones according to either (1) the magnitude of the aggregated values for the O-MAV model or (2) the smaller distances for the TD and OD models. Step 5. Evaluate the performance of each method. The participant entered the rank of his or her own phone in terms of the list of k mobile phones if it was in the list and N/A otherwise. The participants were also encouraged to enter the level of user satisfaction with each method.
ð6aÞ
where the relative weight for the ith specification is 1/rI, which is a standard deviation of the ith specification values. In this paper, we suggest a new way for determining similarity between products. Our method takes into account the personalized ordinal weights of specifications as follows:
Ordinal weighted distance :
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ð6bÞ
i
where wi, i = 1, . . ., n, is the ROC weight. The typical values of the weights are computed from the reciprocal of the standard deviations of the traditional distance measure (Shyu, Haruechaiyasak, & Chen, 2003). For the ordinal weighted distance, they are computed by the ROC method for comparative analysis with the O-MAV method. 4. Experimental evaluation 4.1. Experimental design An experimental study was conducted to compare the performance of three recommendation methods: the traditional distance-based method (TD), the ordinal weight-based distance method (OD), and the ordinal MAV (O-MAV) method. A total of 125 mobile phones offered by a leading Korean code division multiple access (CDMA) carrier, the S Telecom Company, were used for the experiment. The participants in the experiment were 60 students who had purchased mobile phones from the company during the last year (June 2005–June 2006). For the performance evaluation of the recommendation methods, the quality of the decisions resulting from each method was assessed by the accuracy and level
At step 2, the participants did not need to enter all of the specification ranges since this could be considered burdensome. Instead, we asked them to specify at least the two most important specifications in ranges for prior search conditions. To evaluate the quality of the three recommendation methods, we used the performance metrics of accuracy and satisfaction rate. We asked the students to enter information about the specification values of at least the two most important specifications that they considered when they bought their phones. They entered information in the order of the specification’s importance. After the system found the mobile phones that satisfied the students’ preferences as closely as possible, each user was encouraged to enter the rank of his/her own phone among the k mobile phones recommended by the system. Thus, the measure of accuracy was defined as the cumulative hitting ratio of each rank by each method. A higher hit ratio for top ranks in the list indicates that the method suggested the mobile phones that the participants were likely to purchase. We compared the results of each method. For the satisfaction rate, participants were asked to directly enter evaluation scores using a 10-point Likert scale (1 is very inaccurate and 10 is very accurate) after they examined the k recommended phones by accessing the linked product information sites. 4.2. Experimental results We analyzed the experimental results by using statistical methods to compare the average rank and satisfaction rate for the TD, OD, and O-MAV models. We obtained evaluation results from 54 of the 60 students because six students responded that they were unable to find their phones among the recommended products. These phones were not in the list of the 125 phones because the company had stopped marketing them. We therefore excluded these subjects from our analysis of experimental results. For the accuracy measure, the ratio that the students’ own phones were
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5. Concluding remarks
1
TD OD O-MAV
0.9 0.8
Accuracy
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
2
3
4
5
6
7
Rank of Product Recommended Fig. 2. Cumulative accuracy of the three methods.
the same as in the first rank among the k most recommended phones was 29.6% by TD, 48.1% by OD, and 51.9% by the O-MAV model. As shown in Fig. 2, we obtained 100% accuracy after we presented seven and five mobile phones to the participants by TD and OD, respectively. In contrast, the O-MAV model required only four mobile phones. This implies that the O-MAV model captured the participants’ purchasing needs at a lower number of mobile phones. We conducted the Mann–Whitney test for the three paired samples in order to statistically verify the accuracy and satisfaction rates of the three recommendation methods. The values in Table 4 show that the accuracy and satisfaction rates were statistically valid within a significance level of 0.05; the differences between the TD and OD models and between the TD and O-MAV models were thus present. The differences between the two metrics of the OD and O-MAV models were not statistically valid within a significance level of 0.05. We conclude that the methods using ROC weights may provide better performances than the TD in terms of accuracy and satisfaction rate; however, a significant difference between the OD and OMAV models was not found. Methods taking personalized weights into account provided greater accuracy and higher satisfaction rates than others because the O-MAV and OD models were designed to incorporate individual preferences through interviews with participants. In terms of ease of use, the O-MAV model allows customers to provide only rank-order weights; it thus reduces the burden of providing preference information about each product. In particular, 20 of 54 participants entered the range values about 2–4 among the six specifications and expressed that they were satisfied with the results. The O-MAV model is substantially better in terms of computational complexity; the distance-based methods must identify the distances through n(n 1)/2 computations, whereas the O-MAV model only needs to identify n aggregated values. Table 4 Difference test for the accuracy and satisfaction rates of the three methods. MW test
TD OD TD O-MAV OD O-MAV *
Accuracy
Satisfaction rate
Median
Significance level
Median
Significance level
2.0 1.0 2.0 2.0 1.0 2.0
0.0185*
7.0 9.0 7.0 8.5 9.0 8.5
0.0019*
0.0381* 0.7610
Significant at a confidence level of 99.0.
0.0013* 0.9583
The ever increasing online shopping market requires companies to offer personalized recommendations to help customers easily find the products they wish to purchase. We proposed a new recommendation method that attempts to suggest products from simple information, such as ordinal specification weights and specification values. These considerations lead to the ordinal weight-based distance and O-MAV models. In particular, the O-MAV model encourages customers to provide ranges about a subset of specifications; in doing so, it acquires a rank-order of specification weights. We also conducted experimental comparisons between the ordinal weight-based methods and the traditional distance-based method. As a result, these tests consistently show that the proposed method helps the participants select the very products that they want to purchase. Furthermore, participants using our proposed method expressed a high level of satisfaction compared to previously reported recommendation methods. However, this experiment was conducted for mobile phone selection for a limited number of participants. Therefore, further research encompassing other types of product selection problems and participants with diverse demographical properties should be conducted. Acknowledgements This research of the first author was financially supported by the Ministry of Education, Science Technology (MEST) and Korea Institute for Advancement of Technology (KIAT) through the Human Resource Training Project for Regional Innovation. The second author is deeply indebted to Y.S. Kwak for her kind and helpful assistance. References Adomavicius, G., & Tuzhilin, A. (2005). Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734–749. Ahn, B. S., & Park, K. S. (2008). Comparing methods for multiattribute decision making with ordinal weights. Computers and Operations Research, 35, 1660–1670. Balabanovic, M., & Shoham, Y. (1997). FAB: Content-based collaborative recommendation. Communications of the ACM, 40, 66–72. Barron, F. H., & Barret, B. E. (1996). Decision quality using ranked attribute weights. Management Science, 42, 1515–1523. Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Science, 69(Suppl. 32), 1–23. Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23, 329–342. Choi, S. H., & Cho, Y. H. (2004). A utility range-based similar product recommendation algorithm for collaborative companies. Expert Systems with Applications, 27, 549–557. Doorenbos, R., Etzioni, O., & Weld, D. (1997). A scalable comparison-shopping agent for the World Wide Web. In Proceedings of the first international conference on autonomous agents (Agents’97.) (pp. 39–48). Marina del Rey, CA: SIGGRAPH/ SIGART/AIGCHI. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35, 61–70. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J. L., et al. (1999). Combining collaborative filtering with personal agents for better recommendations. In Proc. Conf. Am. Assoc. artificial intelligence (AAAI-99) (pp. 439–446). American Association for Artificial Intelligence. Guttman, R. H., & Maes, P. (1999). Agent-mediated integrative negotiation for retail electronic commerce. Lecture Notes in Computer Science, 1571, 70–90. Guttman, R. H., Moukas, A. G., & Maes, P. (1998). Agent-mediated electronic commerce: A survey. The Knowledge Engineering Review, 13, 147–159. Han, E. H., & Karypis, G. (2005). Feature-based recommendation system. In Proceedings of the 14th ACM international conference on information and knowledge management (pp. 446–452). Bremen, Germany: ACM/SIGIR. Hwang, C. L., & Yoon, K. S. (1980). Multiple attribute decision making methods and applications: A state of art survey. New York: Springer-Verlag. Kirkwood, C. W., & Corner, J. L. (1993). The effectiveness of partial information about attribute weights for ranking alternatives in multiattribute decision making. Organization Behavior and Human Decision Processes, 54, 456–476.
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