Mobile E-commerce model based on social network analysis

The Journal of China Universities of Posts and Telecommunications September 2008, 15(Suppl.): 79–83 www.buptjournal.cn/xben

Mobile E-commerce model based on social network analysis CAI Ya-li1, WANG Wen-dong1, GONG Xiang-yang1, LI Yu-hong1, CHEN Can-feng2, MA Jian2 1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Nokia Research Center, Beijing 100176, China

Abstract

With the development of Internet technology and the increasing popularity of mobile terminals, mobile E-commerce has become a convenient tool in our daily life. In a mobile E-commerce system, how to effectively establish and evaluate trust between seller and buyer is a hotspot. Whether a buyer knows and trusts a seller often strongly influences purchasing decisions, especially in customer-to-customer (C2C) E-commerce or second-hand exchange market. This paper proposes a social network model on the mobile E-commerce system, which can give recommendation values between buyers and sellers in terms of their social relationship, actual geographical distances and historic deal records etc. Experiment results show that the proposed model can effectively help users to make the decision whether and with whom to make a deal by using the recommendation values. In addition, the proposed model also has better security feature than the normal E-commerce system. Keywords

social network analysis (SNA), social influence, E-commerce, interaction history record

1 Introduction As the Internet technologies continue to develop, E-commerce has become prevalent in our daily life. By using E-commerce system, buyers can purchase merchandise from companies or choose second-hand exchange from sellers. Initially, E-commerce systems, based solely on the World Wide Web, depend on the interaction among the users who can access various websites. With the development of increasingly stable mobile platform, mobile E-commerce has migrated onto the mobile platform as portable devices such as smart phones, personal digital assistants (PDA), mobile internet devices (MID), and handheld computers have been adopted rapidly by a wide variety of users [1]. As a result, mobile users are able and more willing to do business on their mobile devices more readily. However, two problems still exist in the current mobile E-commerce system. On the one hand, it’s difficult for consumers to make a decision. Browsing, searching, and buying a product on E-commerce websites are often a time consuming and frustrating task [2]. Over 80% of Web shoppers have left E-commerce websites at some point without finding what they want [3]. According to the research by Sinha and Swearingen [4], a person’s decision to buy a product is often strongly influenced by his or her friends,

acquaintances and business partners, rather than strangers. It is therefore necessary to measure the relationships between buyers and sellers in the social network to evaluate the trust in E-commerce especially in second-hand market. On the other hand, with the rapid adoption of mobile devices, the development of location based services has brought users much convenience. When making deals on mobile devices, buyers may take sellers’ position into account, which will influence their decisions. Thus, it is useful to build an exchange recommendation mechanism based on position information regarding buyers and sellers. Based on the above considerations, this paper suggests a general mobile E-commerce model by taking social networks into account. The social network analysis is applied to build a novel credit recommendation mechanism. In addition, the position information is also considered in the model, which can provide location based services to mobile users. The rest of this paper is organized as follows. A brief review of the research background and related works are given in Sect. 2. Section 3 describes the proposed model and the decision function. Experiment results atop the model are given in Sect. 4. Finally, conclusions and our further works are dedicated in Sect. 5.

2 Background and related works Received date: 02-01-2008 Corresponding author:CAI Ya-li, E-mail: [email protected]

A social network is a social structure between actors,

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mostly individuals or organizations. It indicates the ways in which actors are connected through various social familiarities ranging from casual acquaintance to close familiar bonds [5]. The analysis and applications of social network are based on Six Degree of Separation Theory. The theory refers to the idea that, if a person is one step away from each person he or she knows and two steps away from each person who is known by one of the people he or she knows, then everyone is an average of six ‘step’ away from any person on Earth. Social network analysis is the mapping and measurement of relationships and flows between people, groups, organizations, animals, computers or other information/knowledge processing entities [6]. The nodes in the network are the people and the groups, and the links show relationships or flows between the nodes. Email traffic, disease transmission, and criminal activity can all be modeled by the social network analysis [6]. Some researchers have focused on integrating social network into E-commerce. According to Ref. [7], social network sites including MySpace and Facebook are driving an increasing volume of traffic to retail sites, and are thus becoming a staring point for Web users who are interested in E-commerce. This increase in traffic from social network sites to online retailers shows that highly influential customers directly affect other customers’ decision making. Lam in Ref. [8] built a collaborative recommender system incorporating social network information, called social network in automated collaborative-filtering of knowledge (SNACK). Kim and Srivastava [2] described how to exercise social influence on a customer’s decision making process, provided a summary of technology for social network analysis, and identified the research challenges of measuring and leveraging the impact of social influence on E-commerce decision making. Carroll [9] proposed a typical collaborative filtering algorithm to build a customer’s neighborhood based on his or her preferences of shared products and weighs the interest of neighbors with similar taste to generate new recommendations. According to the research by Hill et al. [10], we can identify potential customers based on existing customers who have bought a service. This was implemented in the domain of telecommunication services. Although some emerging research activities have started to apply social network analysis to E-commerce, they lack a general trust model in mobile E-commerce, especially in C2C E-commerce or second-hand market, where credit values and position information are both important for mobile users to make a market decision. To establish the model, the following problems must be solved as follows: how to introduce and quantify the social relationship among the mobile users in the E-commerce model; how to balance the social relationship and geographical distance between mobile users; how to select influential customers and compute their spread of influence

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through a social network; how to measure the interaction history to inspirit the intending market.

3 Mobile E-commerce modeling General speaking, the mobile E-commerce relationship is modeled as a social network. A mobile E-commerce strategy based on social network is proposed by computing the credit values between users and the influence of some key persons in the network. Meanwhile, the users’ locations are also considered in the model. 3.1 Definitions 1) U , represents the set of users in the system, including buyers and sellers, where U = N . 2) R = {(u, v ) | u, v ∈ U } , represents each pair of users in the system. 3) S ( u, v) , represents the relationship between u and v, where u , v ∈ U . It is defined by the number of ‘step’ between u and v based on Six Degree of Separation Theory. According to the theory, S ( u, v) ∈ {1, 2, 3, 4, 5, 6} . In our system, S ( u, v ) = S ( v, u) . 4) D( u, v ) (t ) , represents the geographical distance between u and v at the time t, where u , v ∈ U . Apparently, given a particular time t1 , D( u, v ) (t1 ) = D( v , u ) (t1 ) . 5) V( u , v ) (t ) , represents the recommendation value between u and v at the time t, where u , v ∈ U . Similarly, given a particular time t1 , V( u, v ) (t1 ) = V( v , u ) ( t1 ) . 3.2 Model establishment The system model is built upon the graph theory, which is widely used in social networks [11]. Figure 1 shows the undirected graph characterizing the system. We use a simple graph G = (U , E ) to describe a social E-commerce network. A graph G consists of a finite set U which represents the vertexes, and a finite set E which represents the edges. E is the set of one-step relationship in the system. We define (u, v ) ∈ E if and only if S ( u, v) = 1.

Fig. 1 Undirected graph model for social E-commerce network

In the graph G, we can see that S ( u, v) is equivalent to the minimum number of edges between any two nodes. For

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CAI Ya-li, et al. / Mobile E-commerce model based on social network analysis

example, in Fig. 1, S (1, 6) = 3 , S (3, 10) = 2 . The positioning information of mobile users should also be taken into account. The existing mobile orientation technology would compute the current distance between user u and v, which is denoted by D( u, v ) (t ) . In general, the value of recommendation V( u , v ) (t ) can be evaluated by S ( u, v) and D( u, v ) (t ) . Since V( u , v ) (t ) increases as S ( u, v) and D( u, v ) (t ) decrease, the value of recommendation between user u and v can be defined as α β V( u, v ) (t ) = + S0 (u , v ) D0 (u , v ) (t )

S (u , v ) S ( w, x)

∑

(1)

(2)

w , x∈U

D0( u , v ) (t ) =

D( u, v ) (t )

∑

w , x∈U

D( w, x ) (t )

(3)

The model above has clearly differentiated credit recommendation values by using social network analysis. However, it ignores the social influence of the middle vertexes in the social network. As an example in Fig. 1, vertex 4 and vertex 1, denoted by pair A , are two-step friends, whose relation path is ‘4->2->1’. Another pair of two-step friends is vertex 4 and vertex 8, denoted by pair B , with the relation path ‘4->7->8’. If the social influences of respective middle vertexes 2 and 7 are widely different in social network, the credit values of pair A and pair B will be not the same. 3.3

vertex 2 has the most one-step friends, thus we consider that user 2 will contribute the largest influence to social network. 2) Q (u , v) , represents the social influence of middle vertexes between user u and v, where u and v are not one-step friends. If vertex u and v are K-step friend (1 < K ≤6 ), we can find out a shortest path u , w1 , w2,..., wK −1 , v , then the influence of middle nodes between vertex u and v is K −1

Q (u, v ) = ∑ F ( wi )

(4)

i =1

In the Eq. (1), α and β are weighting factors. S ( u, v) and D( u, v ) (t ) can be normalized by S0 (u , v ) =

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Social influence of middle nodes

In order to compute the social influence of the middle vertexes, a concept of graph theory, the degree of the vertex in the graph is introduced into this model. The ‘degree’ is not the same as the one in Six ‘Degree’ of Separation Theory. The degree of a vertex in an undirected graph is the number of edges associated with it, except that a loop at a vertex contributes twice to the degree of that vertex [12]. In our social network model, the degree of a vertex shows the number of one-step friends. According to Ref. [13], if vertex i has bigger degree, there are more paths through vertex i to cover the network, so vertex i would contribute larger influence to social network. Degree measures how active or popular a particular vertex is [14, 2]. To measure the social influence of middle vertexes, we have two symbol definitions as follows: 1) F (u) , the degree of vertex, represents the number of user u’s one-step friends. For example, in Fig. 1, F (u2 ) = 5 ,

in which, ( w1 , w2,..., wK −1 )= arg 3.4

max

( w1 , w2 ,..., wK −1 )

Q(u, v)

Interaction history records

In the social E-commerce network, the favourable interaction history records between two persons will increase their credit. The weight of the edge in the graph is thereby introduced into the model. We assign a weight to each edge and model the social E-commerce network as a weighted graph shown as Fig. 2.

Fig. 2 Weighted undirected graph model for social E-commerce network

In Fig. 2, the interaction history records are modelled by assigning numbers of history records to the according edges. To be more specific, a weighted dashed is assigned between two vertexes if they are not one-step friends but have interaction history records. To measure the interaction history records, we introduce two symbol definitions here: 1) B( u, v ) (t ) , the weight of edge, which represents the number of interaction history records between user u and v at the time point t. For example, we assume that the system state at the time t0 is shown in Fig. 2, then B( u2 , u4 ) (t 0 ) = 3 , B( u7 , u10 ) (t 0 ) = 2. 2) H ( u, v ) (t ) , represents the referenced value of interaction histories between user u and v at the time t. Especially, if S ( u, v) = 1 , then H ( u, v ) (t ) = B( u , v ) (t ) . If vertex u and v are K-step friend (1 < K ≤6 ), a shortest path u , w1 , w2,..., wK −1 , v , donated as path P can be found, then we define the referenced interaction histories of middle vertexes along path P can be defined as K −2

BP ( u →v ) (t ) = B( u, w1 ) (t ) + B( wK−1 v ) (t ) + ∑ B( wi wi +1 ) (t ) i =1

(5)

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where ( w1 , w2,..., wK −1 )= arg

max

( w1 , w2 ,..., wK −1 )

BP (u →v ) (t )

If vertex u and v are K-step friend ( 1 < K ≤6 ), the referenced value of interaction histories between vertex u and v is defined as (6) H ( u, v ) (t ) = B( u, v ) ( t ) + γ BP ( u →v ) (t ) In Eq. (6), the direct interaction history record of two vertexes B( u, v ) (t ) is the key element, while the sum of interaction history records of middle vertexes BP ( u→ v ) (t ) is

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50 members and their friendships at the beginning for the experiments. All the members can be buyers and sellers. Therefore, a social E-commerce network is built as shown in Fig. 3. If there are not any interaction history records between two users, the weight to the according edges are not assigned. In the experiment, we consider two priority strategies including social network and location. When different priorities are set, different default values of parameters are listed as Table 1 shows.

an optional element. The range of parameter γ is [0, 1) . 3.5

Decision function

Based on the above analysis, a decision function for the value of recommendation between vertex u and v is defined as α β V( u, v ) (t ) = + + η ⎡⎣Q0 (u , v ) + H 0 ( u, v ) (t )⎤⎦ (7) S0 (u , v ) D0 (u , v ) (t ) In Eq. (7), Q0 (u , v ) and H 0( u , v ) ( t ) can be normalized by Q0 (u , v ) =

Q ( u, v ) ∑ Q( w, x)

(8)

w , x∈U

H 0( u , v ) (t ) =

H ( u , v ) (t )

∑

w , x ∈U

V( u , v ) (t )

(9)

H ( w, x ) (t )

is a decreasing function of

D0( u , v ) (t ) , while V( u , v ) (t )

S0 (u , v )

and

is a increasing function of

Fig. 3 A social E-commerce network in our experiment

Q0 (u , v ) and H 0( u , v ) ( t ) . Three parameters α , β and η

Table 1 Default values of parameters in the experiment

are used to adjust the proportion of different factors respectively, since different users may care different factors when they make a decision. Here α represents the proportion of relationship; β represents the proportion of actual geographical distance; and η shows the proportion of the social influence of middle friends and interaction history records. Our decision function proves to be effective in the experiment result outlined in Sect. 4. The mobile E-commerce model described above provides a general social network model in mobile E-commerce. From the decision function, the recommendation values between buyers and sellers are measured by their relationship, actual geographical distances, middle friends’ social influences and interaction history records. Based on the decision function, buyers can compare different recommendation values of different sellers, and then make a decision whether and with whom to make a deal.

4 Experiments Some experiments are implemented to illustrate the effectiveness of the proposed model. We set the initial about

Parameter Ratio of middle friends’ interaction history records γ Ratio of relation degree α Ratio of actual geographical distance β Ratio of social influence of middle friends and interaction history records η

Priority Social network Location 0.5

0

10 1

2 5

0.5

0.2

Due to the limit of experiment environment, we simulate the location information in rough granularity. As shown in Fig. 3, all the users live in four different districts including A, B, C, D. The distances between two districts are shown in Table 2. The distance between two persons in the same district is 1. Table 2 Distances between any two districts Pair of two districts A and B A and C A and D B and C B and D C and D

Distance 2 2 5 2 3 5

To demonstrate the effectiveness of our mechanism, some vertexes are chosen as several malicious members in the social network. Supposed that malicious members tend to

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CAI Ya-li, et al. / Mobile E-commerce model based on social network analysis

have fewer friends and keep fewer interaction history records with other users. As displayed in Fig. 3, we assume that John, Andrew, Marian and Daniel are malicious members in the social network. Moreover, Jeff and Robert are selected as tested members to compute the recommendation values from other users to identify trustworthy sellers. From Fig. 3, Jeff is an active member in this social network, since he has many one-step friends, while Robert is an inactive member. The recommendation values are obtained from decision function Eq. (7). All the values are normalized to show the proportions that other users may be chosen. The recommendations for an active member are checked first. If Jeff selects the priority of location, the top 20 recommendation values from other users are shown in Fig. 4. In the diagram, the preferred users are all in the same district as Jeff due to the location priority limitation. Notice that, the recommendation value from malicious member John is 5.9%, and ranks the 9th place in the sort order. There exist biggish risks in the real-life E-commerce if users ranked in front of John have no merchandises that Jeff need.

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The recommendations for an inactive member are displayed in Figs. 6 and 7. If Robert selects the priority of Location, the malicious user Marian ranks the 10th place in the sort order. His recommendation for Robert is 3.9%. If Robert selects the priority of social network, there are not any malicious members in the member list. From the comparison of Figs. 6 and 7, we can prove that the mechanism proposed can also take effect and recommend reliable sellers for inactive members in the social E-commerce network.

Fig. 6 Recommendation for Robert by the location priority

Fig. 7 Recommendation for Robert by the social network priority Fig. 4 Recommendation for Jeff by the location prioity

If Jeff selects the priority of social network, the top 20 recommendation values from other users are summarized in Fig. 5. In the diagram, the preferred users all have close relationship with Jeff because of the priority of social network. Meanwhile, users who keep favorite history records and shorter distances with Jeff will be recommended first, such as Ed, Tamara and Amanda. The recommendation value from malicious member John is about 3.7%, and ranked the 19th place in sort order. Compared with Fig. 4, we can find that, for an active member, the risk of meeting malicious member by the priority of social network will be greatly fewer than the priority of location.

Fig. 5 Recommendation for Jeff by the social network priority

5 Conclusions and future works In this paper, a social network model in mobile E-commerce is proposed. In the model, location information is also taken into account to meet mobile users’ demands. And the influence of intermediate friends between buyers and sellers are computed. At the same time, interaction history record is used as an incentive mechanism. Through the suggested decision algorithm, buyers can explicitly identify trustworthy sellers. Experiment results show that the proposed mobile E-commerce model can effectively help users to make decisions by measuring recommendation values of different sellers. Users can also avoid malicious members and increase the security of mobile E-commerce effectively. Our future work aims to improve the robustness and efficiency of the model for a large number of users. The global position system (GPS) module will be used to compute the geographical distances between users more accurately. In addition, more different applications may be implemented based on this model. To p. 97

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China (2007CB310704), and the National Natural Science Foundation of China (90718001).

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From p. 83 Acknowledgements This work is supported by the BUPT-NOKIA Joint Project on Mobile Internet Service, the Hi-Tech Research and Development of China (2007AA01Z206).

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