- Email: [email protected]
The Recommendation Model of MiaoPai Short Video Based on Microblog
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 162 (2019) 331–338 Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
7th International Conference on Information Technology and Quantitative Management 7th International Conference on Information Technology and Quantitative Management (ITQM 2019) (ITQM 2019)
The Recommendation Model of MiaoPai Short Video Based on The Recommendation Model of MiaoPai Short Video Based on Microblog Microblog Manqing Zhuaa, Yue Hea,a,*, Yong Huangaa, Dan Zhangaa Manqing Zhu , Yue He *, Yong Huang , Dan Zhang a
Sichuan University, No. 24 South Section First Ring Road, Chengdu 610064, China Sichuan University, No. 24 South Section First Ring Road, Chengdu 610064, China
a
Abstract Abstract Analyzing and evaluating popularity of short video and then recommending it is beneficial for scientific marketing and Analyzing marketing and evaluating popularity ofarticle short takes videothe and then recommending it is on beneficial scientific marketing and optimizing effectiveness. The microblogs with short video the Sinafor Weibo as the research object optimizing marketing effectiveness. The article takes the microblogs with short on thevideo, Sina Weibo as the research object and apply factor analysis for data analysis to obtain a recommendation modelvideo for short then analyzes and tests the and apply factor analysis formethod. data analysis to obtain a recommendation model for shortcan video, then analyzes tests the model by RS score ranking Through this model, scientific recommendations be made based onand short video model by RS score ranking method. Through this model, scientific recommendations can be made based on short video popularity to improve the audience's viewing interest and achieve better marketing results. The result shows that the model popularity the audience's interestmethod and achieve better marketing results. constructedtobyimprove factor analysis and RSviewing score ranking can effectively recommend the The shortresult video.shows that the model constructed by factor analysis and RS score ranking method can effectively recommend the short video. © © 2020 2019 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open accessPublished article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) © 2019 The Authors. by B.V. of the license Selection and/or peer-review under Elsevier responsibility organizers of ITQM 2019 Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Selection and/or peer-review under responsibility of the organizers of ITQM 2019 Quantitative Management (ITQM 2019) Key words: Microblog Marketing; Popularity of Short Video; Factor Analysis; RS score ranking Key words: Microblog Marketing; Popularity of Short Video; Factor Analysis; RS score ranking
1. Introduction 1. Introduction With the rapid development of the Internet, short video marketing at microblog platform has gradually become With the rapid development of the to Internet, short video marketing microblog platform has a new force in marketing. According the 42nd Statistical Report onatInternet Development in gradually China, upbecome to June a new force in marketing. According to the Statistical Reportand on Internet Development in China, upreached to June 2018, the number of Chinese netizens had42nd reached 802 million, the Internet penetration rate had 2018, the number of Chinese had reached 802 million, penetration reached 57.7%. The 2017 Weibo User netizens Development Report shows that, byand thethe endInternet of September 2017,rate Sinahad Weibo had 57.7%. The active 2017 Weibo User Development Reportdaily shows that,users by the end of September 2017,the Sina Weibo had 376 million users and more than 165 million active respectively. Apparently, influence and 376 million active users andofmore 165 cannot million be daily active users respectively. the influence information dissemination Sinathan Weibo underestimated and ignored.Apparently, It has gradually becomeand an information dissemination of to Sina cannot bevideos underestimated and ignored. It hassince gradually become an approved marketing platform the Weibo public [1]. Short have developed dramatically they appeared in approved marketing platformastoathe public [1]. Short videos have developed dramatically since2.04 theybillion appeared in China from 2013. MiaoPai, representative of those short video platforms, had reached in its China from a representative of users' those penetration short videorate platforms, had reached 2.04 billion in the its average daily2013. videoMiaoPai, playbackas volume in 2017. The was as high as 62.2%, which ranks average daily video playback volume in 2017. The users' penetration rate was as high as 62.2%, which ranks the
* Corresponding author. Tel.: 13008188526. * Corresponding author. Tel.: 13008188526. E-mail address: [email protected]. E-mail address: [email protected].
1877-0509 © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019) 10.1016/j.procs.2019.11.292
332
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
first in all the mobile short video platforms. As the exclusive short video application of Sina Weibo, its rates of exposures and flows are beyond comparison. At present, most researches about short videos are mainly on the theoretical level (e.g. marketing model). Only few studies of the evaluation based on the popularity of short videos can be found. Besides, there is no research with regard to the popularity-based short video recommendation. In practice, marketers often judge the popularity of short videos by experience and make the recommendation accordingly, which may lead to huge inaccuracy and uncertainty. Therefore, there is an urgent demand for short video recommendation models to assist marketing decision-making. In accordance with the characteristics of Microblog marketing and the short videos from MiaoPai, this paper, selecting scientific short video popularity indicators, proposes a recommendation model of the short videos from MiaoPai in Sina Weibo. Overcoming the drawbacks that the playback volume was treated to be the only measurement of popularity, the new model makes quantitative analysis possible. As a result, qualified short videos can be recommended accordingly. 2. Literature review 2.1. Miaopai MiaoPai was firstly launched in August 2013. It is a multi-functional application involving viewing, shooting, editing and sharing. Moreover, it contains a virtual short video community which attracts many celebrities. Simple production, instant messaging, fragmentation of content, and social sharing builds up the most prominent features of MiaoPai [2]. In addition, Sina Weibo has brought incredible rate of flows and added more social attributes to MiaoPai. Under such circumstances, MiaoPai's development is so fast that it has already become a new prevailing media-marketing platform [3]. 2.2. Short video marketing The concept of short video marketing has emerged with the popularity of short video. Widely used by many brands and making great achievements, it has become an emerging force in brand marketing. Canos-Bajo J [4] believes that video marketing can bring more benefits to businesses. Brouwer B [5] proposes that more and more brands are paying attention to video marketing, while Sluis S [6] suggests that online video have already become a key element of marketing. Meanwhile, online video marketing possesses the strengths that traditional marketing methods do not have [7], such as lower marketing threshold, lower production cost, and shorter publicity period [8]. These researches are mainly focusing on theoretical analysis. Although the importance of short video marketing was pointed out successfully, and cases for the short video marketing were provided, only few researches contained the specific evaluation of the effectiveness or some specific analysis with regard to the recommendation. As a consequence, further researches are needed. 2.3. Short video popularity assessment The popularity is one of the most important indicators to reflect the marketing effect of video. The researches on the popularity of traditional video are mature, especially in terms of the definition of popularity. Nonetheless, there are only few researches based on short video popularity. Guan Xiaohui [9] proposes that the popularity of a video refers to the degree of attention that users pay to it. She also points out that indicators like comprehensive video clicks, comments, ratings and others can be used to measure the video popularity. Gabor Szabo [10] chooses video playback to be an indicator of data analysis when
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
333
measuring the popularity of videos on YouTube. In Cheng and other researchers' study [11-12], video playback volume is also used as a main indicator to measure the popularity of video in relevant analysis. The researches mentioned above clarify that the video playback volume can be used as a quantitative measurement of video popularity. However, the assistance for improving short video marketing is limited due to the ignorance of source analysis with regard to video popularity. 2.4. Recommendation of short videos Both foreign and domestic researches can be found in the field of short video recommendation. Based on user behavior analysis, Zhan Yuan [13] suggests a video recommendation algorithm which models user behavior information and modifies user similarity, improving the accuracy of video recommendations. Yin Lutong [14] proposes an analysis method that fuses video reviews. A video recommendation algorithm based on implicit semantic model is also introduced for the same purpose. Lu Shengjun [15] proposes a video recommendation algorithm based on the LDA model of three-layer Bayesian network for the problem of excessive video selection and low conversion rate. Cicekli N K [16] provides a hybrid video recommendation algorithm based on graph model. By coordinating users, establishing contacts between them and associating users with each other according to their relationship, he believes the optimal recommendation can be made. Pazzani M J [17] believes that, based on collaborative filtering, the recommendation algorithm can implement user clustering. Then, supplementary calculations for less relevant users based on users' attribute description information are applied. Most of the above researches are conducted on specific video websites, providing a reference for subsequent researchers. 3. Model construction Since the research subject of this paper are the short videos uploaded through MiaoPai in Weibo, which takes the characteristics of both microblog and short video into account, the research structure in this paper in decided accordingly. Firstly, a recommendation model is established by making use of factor analysis. Then, the RS score method is used to determine the parameters. Finally, a test is conducted to verify the effectiveness of the model. Jiang Shengyi [18] studied the characteristics of Weibo information dissemination, and proposed to evaluate Weibo from user characteristics and information dissemination characteristics, but did not propose specific indicators. Combining the characteristics of this research and short video, the article mainly considers two aspects of indicators: blogger features and microblog content features, then builds blogger influence model and content influence model. 3.1. Blogger influence model The influence of Weibo bloggers determines the popularity of the published Weibo to some extent. Chen Mingliang [19] constructs a blogger influence evaluation index system, which consists of eight indicators including the number of original microblogs and the number of retweets. Meeyoung Cha [20] believes that the number of fans, the number of retweets and the number of mentions is the key factors to the influence of bloggers. However, these features only reflect the information of some bloggers, and can't represent the influence of bloggers well. Therefore, the article analyzes and processes these features, and the number of bloggers, microblogs, and levels is used as indicators to measure the influence of bloggers, as shown in Table 1. Table 1：Indicators of blogger influence model Indicator name Indicator meaning
Number of fans
Number of bloggers' fans
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
334
Number of microblogs
The total number of microblogs published by bloggers
Number of levels
Blogger's levels of Weibo
3.2. Content influence model The influence of Weibo content also affects its popularity to a large extent. The short video itself also affects its popularity. For example, Gao Chong [21] believes that the content of short video has a great influence on the propagation of short video itself, and the duration of short video is an important feature of short video content. In the top ten daily popularity rankings of microblogs including short videos statistical analysis, we found that the short video duration is 3 seconds minimum and 240 seconds maximum. And the average duration is 150 seconds. However, simply using the short video duration as a secondary indicator cannot describe the short video popularity scientifically, so the study needs to standardize the short video duration. We record the standardized video duration as y, and record the video duration as x, namely: 𝑥𝑥/150 … (𝑥𝑥 ≤ 150) 𝑦𝑦 = # (1) 1 − 𝑥𝑥/150 … (𝑥𝑥 > 150) JX Mao [22] used the number of times that a microblog was read and retweeted to measure the ability of users to disseminate information. MA Jun [22] used the number of times a microblog was commented and retweeted as the main information dissemination feature of Weibo. D Boyd [24] considered retweeting as the most important microblog information dissemination behavior. Therefore, the article finally selects the standardized video duration, rate of retweet, rate of comment, and rate of like as the secondary indicators of the microblog content influence, as shown in Table 2. Table 2：Indicators of Content influence model Indicator name Indicator meaning
Standardized video duration
Shown in Eq (1)
Rate of retweet
Number of retweet / Number of fans
Rate of comment
Number of comment / Number of fans
Rate of like
Number of like / Number of fans
After determining the above two model index systems, the factor analysis method is used to construct the blogger influence model and the content influence model respectively. Then the models are integrated into the comprehensive recommendation model by setting parameters. Finally, the RS score ranking method is used to obtain the optimal parameter values and test the model. 4. Empirical analysis The data sets used in the article are all from Sina Weibo. Firstly, the Octopus collector is used to collect the microblogs with short video under the "Miaopai" page of the Weibo search, and then the collected data is cleaned and preprocessed. Finally, the empirical data set is obtained. 4.1. Blogger influence model construction According to the method of previous section, the data obtained after preprocessing is imported into SPSS19.0. Firstly, the KMO test and the Bartlett spherical test are performed on the number of fans, the number of microblogs and the number of levels. The test results show that the KMO value is 0.651 and the statistical significance of the Bartlett spheroid test was is 0.000, which is less than 1%, indicating that the data is correlated
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
335
and suitable for factor analysis. Next, the dimensionality reduction process is performed. The results are shown in Table 3:
Component
Table 3：Total variance of interpretation-1 Initial eigenvalue Extraction Sums of Squared Loadings Sum
% of Variance
% of Accumulation
Sum
% of Variance
% of Accumulation
1
1.824
60.813
60.813
1.824
60.813
60.813
2
0.660
21.991
82.804
3
0.516
17.196
100.00
It can be seen from Table 3 that a factor can be extracted and the eigen value is high, indicating that the number of fans, the number of microblogs and the number of levels contribute the most to the interpretation of the original variables, reaching 60.8%, so it is considered that these three indicators can fully explain the blogger influence factor. Further analysis can obtain the following component matrix, as shown in Table 4: Table 4：Component matrix-1 Component 1 Number of fans (X1)
0.744
Number of Weibo (X2)
0.820
Number of levels (X3)
0.774
Table 4 shows the scores of each factor, so the blogger influence model is shown in Eq (2): 𝐹𝐹0 = 0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 (2) In Eq (2), F1 represents blogger influence. X1 represents the number of fans. X2 represents the number of microblogs. X3 represents the number of levels. 4.2. Content influence model construction Firstly, the KMO test and the Bartlett spherical test are performed on the standardized video duration, rate of retweet, rate of comment and rate of like. The test results show that the KMO value is 0.764, and the Bartlett spherical test has a statistical significance of 0.000, less than 1%, indicating the data is correlated and suitable for factor analysis. The next step is to reduce the dimensionality. The results are shown in Table 5: Component
Table 5：Total variance of interpretation-2 Initial eigenvalue Extraction Sums of Squared Loadings Sum
% of Variance
% of Accumulation
Sum
% of Variance
% of Accumulation
1
2.894
72.357
72.357
2.894
72.357
72.357
2
1.000
25.000
97.358
1.000
25.000
97.358
3
0.081
2.019
99.376
4
0.025
0.624
100.00
It can be seen from Table 5 that the first two factors have an eigen value greater than 1, and the cumulative contribution exceeds 97%, so the two factors can be extracted. Therefore, the component matrix is further analyzed, as shown in Table 6. Table 6：Component matrix-2 Component 1
Component 2
336
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000 Standardized video duration (X4)
0.004
1.000
Rate of retweet (X5)
0.973
0.001
0.986
-0.005
0.988
0.000
Rate of comment (X6) Rate of like (X7)
Table 6 shows the factor scores. Combined with Table 5, the content influence model can be calculated as shown in Eq (3): 𝐹𝐹8 = 0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? (3) In Eq (3), F2 represents the influence of blog post. X4 represents the duration of the standardized video. X5 represents the rate of retweet. X6 represents the rate of comment. X7 represents the rate of like. 4.3. Comprehensive recommendation model construction Through the above analysis, the final comprehensive recommendation model is shown in Eq (4): 𝐹𝐹 = （0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 )@ × (0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? )0B@ (4) In Eq (4), F denotes the comprehensive recommended popularity. And α is the parameter to adjust the blogger influence and content influence. When α=1, it means that the recommended popularity is only related to the influence of the blogger. When α=0, it means that the recommended popularity is only related to the content influence. The value of α will be determined in the next experiment. (1) RS score ranking Some scholars proposed the RS score ranking method [25], and its calculation method is as shown in Eq (5): 𝑃𝑃(𝑡𝑡)I 𝑅𝑅𝑅𝑅 = ∑JKL0 (5) 𝐿𝐿 In Eq (5), P(t) represents the position of the recommended microblog on the recommendation list of the target user, and L represents the length of the recommendation list, and n is the number of recommended microblogs. Therefore, the smaller the RS score is, the better the performance of the algorithm has. According to the above method, the article collects the microblog with short video under the category of "Miaopai" page of Weibo search. Firstly, we calculate the recommended popularity of microblog when α is in the range of 0 to 1 and the step length is 0.01. Then the microblogs are ranked according to the recommended popularity. Next, 100 experimenters are invited to score the recommended rankings. Finally, the RS score of the recommended list length L is 1000 and n is 20, and the final RS score is the average RS score of 100 experimenters' scores. The regression analysis of the calculated RS score and the corresponding value of α shows that the curve fitting has the best effect, and the adjusted R2 is 0.918. Therefore, it can be considered that the RS score and the α value are in quadratic linearity regression, the fitting equation is as shown in Eq (6): (6) 𝑅𝑅𝑅𝑅 = 0.172 × 𝛼𝛼 8 − 0.192 × 𝛼𝛼 + 0.066 It can be obtained from Eq (6) that when α is 0.56, the minimum value of the RS score is 0.012. Therefore, the value of α in the comprehensive recommendation Eq (4) is determined to be 0.56. When the recommended list fully matches the user's reading order, the RS score is 0.0105, which is very close to the minimum RS score of 0.012. Therefore, the experimental results are consistent with the actual situation. The final comprehensive recommendation model is shown in Eq (7): 𝐹𝐹 = （0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 )P.<> × (0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? )P.;; (7) (2) Model evaluation Many scholars used the recall rate and accuracy to evaluate the model in research. Therefore, the article also uses these two indicators to evaluate the comprehensive recommendation model. The formula for calculating the indicators is shown in Eq (8) and (9): UVWXYYVJZVZ \JZ ]KV^VZ YKW_X`aXbc (8) 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = dXe\a JfY`V_ Xg YKW_X`aXbc ]KV^VZ `h fcV_c
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =
UVWXYYVJZVZ \JZ ]KV^VZ YKW_X`aXbc
dXe\a JfY`V_ Xg YKW_X`aXbc _VWXYYVJZVZ eX fcV_c
337
(9)
According to the method in the model construction process, the recall rate and accuracy are calculated separately. The final experimental result shows that the recall rate is 76.46% and the accuracy is 86.53%, indicating that the recommended model can accurately recommend short video to users. 5. Conclusion Based on the Sina Weibo platform, this study selects microblogs with short video as the research object, and the factor analysis method is applied to establish the short video recommendation model. Then the RS score ranking method is used to analyze and test the model. Overcoming the shortcomings of recommendation in the previous research, this paper analyzes the blogger influence and content influence, and determined the parameters to construct a scientific short video popularity recommendation model. What's more, it provides a basis for decision-making to improve the short video marketing effects. The main innovations of this paper are as follows: l A supplement to the current short video popularity study. Most of the existing researches on short video popularity are aimed at a certain website or platform. There are few studies that integrate two platforms. In this paper, the short video of Miaopai based on Weibo platform was studied, and the characteristics of microblogs and short videos were combined to evaluate the popularity of short video. This research helps marketers understand the source of short video popularity and leads to more scientific and effective marketing. l After constructing the model by factor analysis, the model parameters are determined by RS score method. Using the combination of these two methods, the influence of accidental factors is eliminated, and the model can be constructed more scientifically. The final model evaluation results also prove that the model has better performance. Although the article has achieved certain results, it still has some shortcomings. In the future, we can further consider adding time series factors to study the influence of time factors. We can also consider classifying short videos, and extract short video types users may be interested with. Then we can recommend short video based on popularity and classification. In addition, we can classify users and invite different types of users to rank recommended microblogs, and study different users' preferences for short videos. Acknowledgements We would like to acknowledge the financial support of Sichuan University Innovation Spark Project 2018(Project No.2018hhf-41) for the completion of this paper. References [1] Li Xinlei, Wang Hao, Liu Xiaomin, Deng Sanhong. Comparing text vector generators for weibo short text classification[J]. Data Analysis and Knowledge Discovery,2018,2(08):41-50. [2] Long Teng. The development of short video in Miaopai in the new media environment[J]. Electronic Technology & Software Engineering,2017(21):66. [3] Chen Minghua. The current situation and optimization strategy of short video transmission in Miaopai[J]. Journal of Social Science of Harbin Normal University,2017(03):150-152. [4] Canas-Bajo J, Silvennoinen J M. Cross-cultural factors in experiencing online video contents in product marketing[J]. International Journal of Art, Culture and Design Technologies,2017,6(1):40-56. [5] Brouwer B. Three significant video trends for perfecting your 2018 content marketing strategy[J]. EContent,2017, 40(6):37.
338
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
[6] Sluis S. More brands invest in video marketing[J]. Customer Relationship Management,2014,18(3). [7] Li Ziqing, Tan Deqing. The choice of advertising pricing model for network video operators[J]. Statistics & Decision,2017(02):54-57. [8] Bai Haotian. "Seven seconds" marketing - talking about short video marketing[J]. Journalism & Communication, 2016(06):48-49. [9] Guan Xiaohui, Qian Yaguan, Zhou Zhiming, et al. Research on user original video hotness under content distribution network[J]. 2013(08):52-55. [10] Szabo G, Huberman B A. Predicting the popularity of online content[J]. Communications of the ACM,2010,53(8). [11] Cheng X, Dale C, Liu J. Statistics and social network of Youtube videos: International Workshop on Quality of Service,2008[C]. [12] Ouyang S, Li C, Li X, et al. Analyzing the dynamics of online video popularity[J]. Journal of China Universities of Posts & Telecommunications,2017,24(3):58-69. [13] Zhan Yuan, Wang Yu. Video recommendation algorithm research based on user behavior analysis[J]. Electronic Measurement Technology,2017,40(04):39-42. [14] Yin Lutong, Yu Jiong, Lu Liang, et al. Video recommendation algorithm fusing comment analysis and latent factor model[J]. Journal of Computer Applications, 2015, 35(11):3247-3251. [15] Lu Shengqi, Guan Lian, Jin Min, et al. The application of LDA in online video recommendation[J]. Information Technology and Network Security,2016,35(11):74-79. [16] Gizem Öztürk, Cicekli N K. A Hybrid Video Recommendation System Using a Graph-Based Algorithm[C]// International Conference on Industrial Engineering & Other Applications of Applied Intelligent Systems Conference on Modern Approaches in Applied Intelligence. Springer-Verlag, 2011. [17] Pazzani M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial Intelligence Review, 1999, 13(56):393-408. [18] Jiang Shengyi, Chen Dongyi, Pang Guansong, et al. A summary of research on credibility analysis of Weibo information[J]. Library and Information Service ,2013(12):136-142. [19] Chen Mingliang, Qiu Tingting, Xie Ying. Scientific construction of microblog's main influence evaluation index system[J]. Journal of Zhejiang University (Humanities and Social Sciences), 2014(02):53-63. [20] Cha M, Haddadi H, Benevenuto F, et al. Measuring user influence in Twitter: the million follower fallacy: International Conference on Weblogs and Social Media, Icwsm 2010, Washington, Dc, Usa, May,2010[C]. [21] Gao Chong, Yang Boxu. Analysis of content production mode of micro video-based on the research of Sina Weibo official short video application "Miaopai"[J]. Journalism and Mass Communication Monthly,2016(23):60-65. [22] Mao J X, Liu Y Q, Zhang M, et al. Social influence analysis for Microblog user based on user behavior[J]. Chinese Journal of Computers,2014. [23] Jun M A, Zhou G, Bin X U, et al. Analysis of user influence in Microblog based on individual attribute features[J]. Application Research of Computers,2013,30(8):2483-2487. [24] Boyd D, Golder S, Lotan G. Tweet, tweet, retweet: conversational aspects of retweeting on Twitter: Hawaii International Conference on System Sciences,2010[C]. [25] Jie L, Yun L, Zhen-jiang Z, Chang-ni G. Personalized recommendation via an improved NBI algorithm and user influence model in a Microblog network[J]. Physica A: Statistical Mechanics and its Applications. 2013, 392(19):4594-4605.
ScienceDirect Procedia Computer Science 162 (2019) 331–338 Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
7th International Conference on Information Technology and Quantitative Management 7th International Conference on Information Technology and Quantitative Management (ITQM 2019) (ITQM 2019)
The Recommendation Model of MiaoPai Short Video Based on The Recommendation Model of MiaoPai Short Video Based on Microblog Microblog Manqing Zhuaa, Yue Hea,a,*, Yong Huangaa, Dan Zhangaa Manqing Zhu , Yue He *, Yong Huang , Dan Zhang a
Sichuan University, No. 24 South Section First Ring Road, Chengdu 610064, China Sichuan University, No. 24 South Section First Ring Road, Chengdu 610064, China
a
Abstract Abstract Analyzing and evaluating popularity of short video and then recommending it is beneficial for scientific marketing and Analyzing marketing and evaluating popularity ofarticle short takes videothe and then recommending it is on beneficial scientific marketing and optimizing effectiveness. The microblogs with short video the Sinafor Weibo as the research object optimizing marketing effectiveness. The article takes the microblogs with short on thevideo, Sina Weibo as the research object and apply factor analysis for data analysis to obtain a recommendation modelvideo for short then analyzes and tests the and apply factor analysis formethod. data analysis to obtain a recommendation model for shortcan video, then analyzes tests the model by RS score ranking Through this model, scientific recommendations be made based onand short video model by RS score ranking method. Through this model, scientific recommendations can be made based on short video popularity to improve the audience's viewing interest and achieve better marketing results. The result shows that the model popularity the audience's interestmethod and achieve better marketing results. constructedtobyimprove factor analysis and RSviewing score ranking can effectively recommend the The shortresult video.shows that the model constructed by factor analysis and RS score ranking method can effectively recommend the short video. © © 2020 2019 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open accessPublished article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) © 2019 The Authors. by B.V. of the license Selection and/or peer-review under Elsevier responsibility organizers of ITQM 2019 Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Selection and/or peer-review under responsibility of the organizers of ITQM 2019 Quantitative Management (ITQM 2019) Key words: Microblog Marketing; Popularity of Short Video; Factor Analysis; RS score ranking Key words: Microblog Marketing; Popularity of Short Video; Factor Analysis; RS score ranking
1. Introduction 1. Introduction With the rapid development of the Internet, short video marketing at microblog platform has gradually become With the rapid development of the to Internet, short video marketing microblog platform has a new force in marketing. According the 42nd Statistical Report onatInternet Development in gradually China, upbecome to June a new force in marketing. According to the Statistical Reportand on Internet Development in China, upreached to June 2018, the number of Chinese netizens had42nd reached 802 million, the Internet penetration rate had 2018, the number of Chinese had reached 802 million, penetration reached 57.7%. The 2017 Weibo User netizens Development Report shows that, byand thethe endInternet of September 2017,rate Sinahad Weibo had 57.7%. The active 2017 Weibo User Development Reportdaily shows that,users by the end of September 2017,the Sina Weibo had 376 million users and more than 165 million active respectively. Apparently, influence and 376 million active users andofmore 165 cannot million be daily active users respectively. the influence information dissemination Sinathan Weibo underestimated and ignored.Apparently, It has gradually becomeand an information dissemination of to Sina cannot bevideos underestimated and ignored. It hassince gradually become an approved marketing platform the Weibo public [1]. Short have developed dramatically they appeared in approved marketing platformastoathe public [1]. Short videos have developed dramatically since2.04 theybillion appeared in China from 2013. MiaoPai, representative of those short video platforms, had reached in its China from a representative of users' those penetration short videorate platforms, had reached 2.04 billion in the its average daily2013. videoMiaoPai, playbackas volume in 2017. The was as high as 62.2%, which ranks average daily video playback volume in 2017. The users' penetration rate was as high as 62.2%, which ranks the
* Corresponding author. Tel.: 13008188526. * Corresponding author. Tel.: 13008188526. E-mail address: [email protected]. E-mail address: [email protected].
1877-0509 © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019) 10.1016/j.procs.2019.11.292
332
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
first in all the mobile short video platforms. As the exclusive short video application of Sina Weibo, its rates of exposures and flows are beyond comparison. At present, most researches about short videos are mainly on the theoretical level (e.g. marketing model). Only few studies of the evaluation based on the popularity of short videos can be found. Besides, there is no research with regard to the popularity-based short video recommendation. In practice, marketers often judge the popularity of short videos by experience and make the recommendation accordingly, which may lead to huge inaccuracy and uncertainty. Therefore, there is an urgent demand for short video recommendation models to assist marketing decision-making. In accordance with the characteristics of Microblog marketing and the short videos from MiaoPai, this paper, selecting scientific short video popularity indicators, proposes a recommendation model of the short videos from MiaoPai in Sina Weibo. Overcoming the drawbacks that the playback volume was treated to be the only measurement of popularity, the new model makes quantitative analysis possible. As a result, qualified short videos can be recommended accordingly. 2. Literature review 2.1. Miaopai MiaoPai was firstly launched in August 2013. It is a multi-functional application involving viewing, shooting, editing and sharing. Moreover, it contains a virtual short video community which attracts many celebrities. Simple production, instant messaging, fragmentation of content, and social sharing builds up the most prominent features of MiaoPai [2]. In addition, Sina Weibo has brought incredible rate of flows and added more social attributes to MiaoPai. Under such circumstances, MiaoPai's development is so fast that it has already become a new prevailing media-marketing platform [3]. 2.2. Short video marketing The concept of short video marketing has emerged with the popularity of short video. Widely used by many brands and making great achievements, it has become an emerging force in brand marketing. Canos-Bajo J [4] believes that video marketing can bring more benefits to businesses. Brouwer B [5] proposes that more and more brands are paying attention to video marketing, while Sluis S [6] suggests that online video have already become a key element of marketing. Meanwhile, online video marketing possesses the strengths that traditional marketing methods do not have [7], such as lower marketing threshold, lower production cost, and shorter publicity period [8]. These researches are mainly focusing on theoretical analysis. Although the importance of short video marketing was pointed out successfully, and cases for the short video marketing were provided, only few researches contained the specific evaluation of the effectiveness or some specific analysis with regard to the recommendation. As a consequence, further researches are needed. 2.3. Short video popularity assessment The popularity is one of the most important indicators to reflect the marketing effect of video. The researches on the popularity of traditional video are mature, especially in terms of the definition of popularity. Nonetheless, there are only few researches based on short video popularity. Guan Xiaohui [9] proposes that the popularity of a video refers to the degree of attention that users pay to it. She also points out that indicators like comprehensive video clicks, comments, ratings and others can be used to measure the video popularity. Gabor Szabo [10] chooses video playback to be an indicator of data analysis when
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
333
measuring the popularity of videos on YouTube. In Cheng and other researchers' study [11-12], video playback volume is also used as a main indicator to measure the popularity of video in relevant analysis. The researches mentioned above clarify that the video playback volume can be used as a quantitative measurement of video popularity. However, the assistance for improving short video marketing is limited due to the ignorance of source analysis with regard to video popularity. 2.4. Recommendation of short videos Both foreign and domestic researches can be found in the field of short video recommendation. Based on user behavior analysis, Zhan Yuan [13] suggests a video recommendation algorithm which models user behavior information and modifies user similarity, improving the accuracy of video recommendations. Yin Lutong [14] proposes an analysis method that fuses video reviews. A video recommendation algorithm based on implicit semantic model is also introduced for the same purpose. Lu Shengjun [15] proposes a video recommendation algorithm based on the LDA model of three-layer Bayesian network for the problem of excessive video selection and low conversion rate. Cicekli N K [16] provides a hybrid video recommendation algorithm based on graph model. By coordinating users, establishing contacts between them and associating users with each other according to their relationship, he believes the optimal recommendation can be made. Pazzani M J [17] believes that, based on collaborative filtering, the recommendation algorithm can implement user clustering. Then, supplementary calculations for less relevant users based on users' attribute description information are applied. Most of the above researches are conducted on specific video websites, providing a reference for subsequent researchers. 3. Model construction Since the research subject of this paper are the short videos uploaded through MiaoPai in Weibo, which takes the characteristics of both microblog and short video into account, the research structure in this paper in decided accordingly. Firstly, a recommendation model is established by making use of factor analysis. Then, the RS score method is used to determine the parameters. Finally, a test is conducted to verify the effectiveness of the model. Jiang Shengyi [18] studied the characteristics of Weibo information dissemination, and proposed to evaluate Weibo from user characteristics and information dissemination characteristics, but did not propose specific indicators. Combining the characteristics of this research and short video, the article mainly considers two aspects of indicators: blogger features and microblog content features, then builds blogger influence model and content influence model. 3.1. Blogger influence model The influence of Weibo bloggers determines the popularity of the published Weibo to some extent. Chen Mingliang [19] constructs a blogger influence evaluation index system, which consists of eight indicators including the number of original microblogs and the number of retweets. Meeyoung Cha [20] believes that the number of fans, the number of retweets and the number of mentions is the key factors to the influence of bloggers. However, these features only reflect the information of some bloggers, and can't represent the influence of bloggers well. Therefore, the article analyzes and processes these features, and the number of bloggers, microblogs, and levels is used as indicators to measure the influence of bloggers, as shown in Table 1. Table 1：Indicators of blogger influence model Indicator name Indicator meaning
Number of fans
Number of bloggers' fans
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
334
Number of microblogs
The total number of microblogs published by bloggers
Number of levels
Blogger's levels of Weibo
3.2. Content influence model The influence of Weibo content also affects its popularity to a large extent. The short video itself also affects its popularity. For example, Gao Chong [21] believes that the content of short video has a great influence on the propagation of short video itself, and the duration of short video is an important feature of short video content. In the top ten daily popularity rankings of microblogs including short videos statistical analysis, we found that the short video duration is 3 seconds minimum and 240 seconds maximum. And the average duration is 150 seconds. However, simply using the short video duration as a secondary indicator cannot describe the short video popularity scientifically, so the study needs to standardize the short video duration. We record the standardized video duration as y, and record the video duration as x, namely: 𝑥𝑥/150 … (𝑥𝑥 ≤ 150) 𝑦𝑦 = # (1) 1 − 𝑥𝑥/150 … (𝑥𝑥 > 150) JX Mao [22] used the number of times that a microblog was read and retweeted to measure the ability of users to disseminate information. MA Jun [22] used the number of times a microblog was commented and retweeted as the main information dissemination feature of Weibo. D Boyd [24] considered retweeting as the most important microblog information dissemination behavior. Therefore, the article finally selects the standardized video duration, rate of retweet, rate of comment, and rate of like as the secondary indicators of the microblog content influence, as shown in Table 2. Table 2：Indicators of Content influence model Indicator name Indicator meaning
Standardized video duration
Shown in Eq (1)
Rate of retweet
Number of retweet / Number of fans
Rate of comment
Number of comment / Number of fans
Rate of like
Number of like / Number of fans
After determining the above two model index systems, the factor analysis method is used to construct the blogger influence model and the content influence model respectively. Then the models are integrated into the comprehensive recommendation model by setting parameters. Finally, the RS score ranking method is used to obtain the optimal parameter values and test the model. 4. Empirical analysis The data sets used in the article are all from Sina Weibo. Firstly, the Octopus collector is used to collect the microblogs with short video under the "Miaopai" page of the Weibo search, and then the collected data is cleaned and preprocessed. Finally, the empirical data set is obtained. 4.1. Blogger influence model construction According to the method of previous section, the data obtained after preprocessing is imported into SPSS19.0. Firstly, the KMO test and the Bartlett spherical test are performed on the number of fans, the number of microblogs and the number of levels. The test results show that the KMO value is 0.651 and the statistical significance of the Bartlett spheroid test was is 0.000, which is less than 1%, indicating that the data is correlated
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
335
and suitable for factor analysis. Next, the dimensionality reduction process is performed. The results are shown in Table 3:
Component
Table 3：Total variance of interpretation-1 Initial eigenvalue Extraction Sums of Squared Loadings Sum
% of Variance
% of Accumulation
Sum
% of Variance
% of Accumulation
1
1.824
60.813
60.813
1.824
60.813
60.813
2
0.660
21.991
82.804
3
0.516
17.196
100.00
It can be seen from Table 3 that a factor can be extracted and the eigen value is high, indicating that the number of fans, the number of microblogs and the number of levels contribute the most to the interpretation of the original variables, reaching 60.8%, so it is considered that these three indicators can fully explain the blogger influence factor. Further analysis can obtain the following component matrix, as shown in Table 4: Table 4：Component matrix-1 Component 1 Number of fans (X1)
0.744
Number of Weibo (X2)
0.820
Number of levels (X3)
0.774
Table 4 shows the scores of each factor, so the blogger influence model is shown in Eq (2): 𝐹𝐹0 = 0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 (2) In Eq (2), F1 represents blogger influence. X1 represents the number of fans. X2 represents the number of microblogs. X3 represents the number of levels. 4.2. Content influence model construction Firstly, the KMO test and the Bartlett spherical test are performed on the standardized video duration, rate of retweet, rate of comment and rate of like. The test results show that the KMO value is 0.764, and the Bartlett spherical test has a statistical significance of 0.000, less than 1%, indicating the data is correlated and suitable for factor analysis. The next step is to reduce the dimensionality. The results are shown in Table 5: Component
Table 5：Total variance of interpretation-2 Initial eigenvalue Extraction Sums of Squared Loadings Sum
% of Variance
% of Accumulation
Sum
% of Variance
% of Accumulation
1
2.894
72.357
72.357
2.894
72.357
72.357
2
1.000
25.000
97.358
1.000
25.000
97.358
3
0.081
2.019
99.376
4
0.025
0.624
100.00
It can be seen from Table 5 that the first two factors have an eigen value greater than 1, and the cumulative contribution exceeds 97%, so the two factors can be extracted. Therefore, the component matrix is further analyzed, as shown in Table 6. Table 6：Component matrix-2 Component 1
Component 2
336
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000 Standardized video duration (X4)
0.004
1.000
Rate of retweet (X5)
0.973
0.001
0.986
-0.005
0.988
0.000
Rate of comment (X6) Rate of like (X7)
Table 6 shows the factor scores. Combined with Table 5, the content influence model can be calculated as shown in Eq (3): 𝐹𝐹8 = 0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? (3) In Eq (3), F2 represents the influence of blog post. X4 represents the duration of the standardized video. X5 represents the rate of retweet. X6 represents the rate of comment. X7 represents the rate of like. 4.3. Comprehensive recommendation model construction Through the above analysis, the final comprehensive recommendation model is shown in Eq (4): 𝐹𝐹 = （0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 )@ × (0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? )0B@ (4) In Eq (4), F denotes the comprehensive recommended popularity. And α is the parameter to adjust the blogger influence and content influence. When α=1, it means that the recommended popularity is only related to the influence of the blogger. When α=0, it means that the recommended popularity is only related to the content influence. The value of α will be determined in the next experiment. (1) RS score ranking Some scholars proposed the RS score ranking method [25], and its calculation method is as shown in Eq (5): 𝑃𝑃(𝑡𝑡)I 𝑅𝑅𝑅𝑅 = ∑JKL0 (5) 𝐿𝐿 In Eq (5), P(t) represents the position of the recommended microblog on the recommendation list of the target user, and L represents the length of the recommendation list, and n is the number of recommended microblogs. Therefore, the smaller the RS score is, the better the performance of the algorithm has. According to the above method, the article collects the microblog with short video under the category of "Miaopai" page of Weibo search. Firstly, we calculate the recommended popularity of microblog when α is in the range of 0 to 1 and the step length is 0.01. Then the microblogs are ranked according to the recommended popularity. Next, 100 experimenters are invited to score the recommended rankings. Finally, the RS score of the recommended list length L is 1000 and n is 20, and the final RS score is the average RS score of 100 experimenters' scores. The regression analysis of the calculated RS score and the corresponding value of α shows that the curve fitting has the best effect, and the adjusted R2 is 0.918. Therefore, it can be considered that the RS score and the α value are in quadratic linearity regression, the fitting equation is as shown in Eq (6): (6) 𝑅𝑅𝑅𝑅 = 0.172 × 𝛼𝛼 8 − 0.192 × 𝛼𝛼 + 0.066 It can be obtained from Eq (6) that when α is 0.56, the minimum value of the RS score is 0.012. Therefore, the value of α in the comprehensive recommendation Eq (4) is determined to be 0.56. When the recommended list fully matches the user's reading order, the RS score is 0.0105, which is very close to the minimum RS score of 0.012. Therefore, the experimental results are consistent with the actual situation. The final comprehensive recommendation model is shown in Eq (7): 𝐹𝐹 = （0.74𝑋𝑋0 + 0.82𝑋𝑋8 + 0.77𝑋𝑋9 )P.<> × (0.26𝑋𝑋; + 0.72𝑋𝑋< + 0.73𝑋𝑋> + 0.73𝑋𝑋? )P.;; (7) (2) Model evaluation Many scholars used the recall rate and accuracy to evaluate the model in research. Therefore, the article also uses these two indicators to evaluate the comprehensive recommendation model. The formula for calculating the indicators is shown in Eq (8) and (9): UVWXYYVJZVZ \JZ ]KV^VZ YKW_X`aXbc (8) 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = dXe\a JfY`V_ Xg YKW_X`aXbc ]KV^VZ `h fcV_c
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =
UVWXYYVJZVZ \JZ ]KV^VZ YKW_X`aXbc
dXe\a JfY`V_ Xg YKW_X`aXbc _VWXYYVJZVZ eX fcV_c
337
(9)
According to the method in the model construction process, the recall rate and accuracy are calculated separately. The final experimental result shows that the recall rate is 76.46% and the accuracy is 86.53%, indicating that the recommended model can accurately recommend short video to users. 5. Conclusion Based on the Sina Weibo platform, this study selects microblogs with short video as the research object, and the factor analysis method is applied to establish the short video recommendation model. Then the RS score ranking method is used to analyze and test the model. Overcoming the shortcomings of recommendation in the previous research, this paper analyzes the blogger influence and content influence, and determined the parameters to construct a scientific short video popularity recommendation model. What's more, it provides a basis for decision-making to improve the short video marketing effects. The main innovations of this paper are as follows: l A supplement to the current short video popularity study. Most of the existing researches on short video popularity are aimed at a certain website or platform. There are few studies that integrate two platforms. In this paper, the short video of Miaopai based on Weibo platform was studied, and the characteristics of microblogs and short videos were combined to evaluate the popularity of short video. This research helps marketers understand the source of short video popularity and leads to more scientific and effective marketing. l After constructing the model by factor analysis, the model parameters are determined by RS score method. Using the combination of these two methods, the influence of accidental factors is eliminated, and the model can be constructed more scientifically. The final model evaluation results also prove that the model has better performance. Although the article has achieved certain results, it still has some shortcomings. In the future, we can further consider adding time series factors to study the influence of time factors. We can also consider classifying short videos, and extract short video types users may be interested with. Then we can recommend short video based on popularity and classification. In addition, we can classify users and invite different types of users to rank recommended microblogs, and study different users' preferences for short videos. Acknowledgements We would like to acknowledge the financial support of Sichuan University Innovation Spark Project 2018(Project No.2018hhf-41) for the completion of this paper. References [1] Li Xinlei, Wang Hao, Liu Xiaomin, Deng Sanhong. Comparing text vector generators for weibo short text classification[J]. Data Analysis and Knowledge Discovery,2018,2(08):41-50. [2] Long Teng. The development of short video in Miaopai in the new media environment[J]. Electronic Technology & Software Engineering,2017(21):66. [3] Chen Minghua. The current situation and optimization strategy of short video transmission in Miaopai[J]. Journal of Social Science of Harbin Normal University,2017(03):150-152. [4] Canas-Bajo J, Silvennoinen J M. Cross-cultural factors in experiencing online video contents in product marketing[J]. International Journal of Art, Culture and Design Technologies,2017,6(1):40-56. [5] Brouwer B. Three significant video trends for perfecting your 2018 content marketing strategy[J]. EContent,2017, 40(6):37.
338
Manqing Zhu et al. / Procedia Computer Science 162 (2019) 331–338 Manqing Zhu/ Procedia Computer Science 00 (2019) 000–000
[6] Sluis S. More brands invest in video marketing[J]. Customer Relationship Management,2014,18(3). [7] Li Ziqing, Tan Deqing. The choice of advertising pricing model for network video operators[J]. Statistics & Decision,2017(02):54-57. [8] Bai Haotian. "Seven seconds" marketing - talking about short video marketing[J]. Journalism & Communication, 2016(06):48-49. [9] Guan Xiaohui, Qian Yaguan, Zhou Zhiming, et al. Research on user original video hotness under content distribution network[J]. 2013(08):52-55. [10] Szabo G, Huberman B A. Predicting the popularity of online content[J]. Communications of the ACM,2010,53(8). [11] Cheng X, Dale C, Liu J. Statistics and social network of Youtube videos: International Workshop on Quality of Service,2008[C]. [12] Ouyang S, Li C, Li X, et al. Analyzing the dynamics of online video popularity[J]. Journal of China Universities of Posts & Telecommunications,2017,24(3):58-69. [13] Zhan Yuan, Wang Yu. Video recommendation algorithm research based on user behavior analysis[J]. Electronic Measurement Technology,2017,40(04):39-42. [14] Yin Lutong, Yu Jiong, Lu Liang, et al. Video recommendation algorithm fusing comment analysis and latent factor model[J]. Journal of Computer Applications, 2015, 35(11):3247-3251. [15] Lu Shengqi, Guan Lian, Jin Min, et al. The application of LDA in online video recommendation[J]. Information Technology and Network Security,2016,35(11):74-79. [16] Gizem Öztürk, Cicekli N K. A Hybrid Video Recommendation System Using a Graph-Based Algorithm[C]// International Conference on Industrial Engineering & Other Applications of Applied Intelligent Systems Conference on Modern Approaches in Applied Intelligence. Springer-Verlag, 2011. [17] Pazzani M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial Intelligence Review, 1999, 13(56):393-408. [18] Jiang Shengyi, Chen Dongyi, Pang Guansong, et al. A summary of research on credibility analysis of Weibo information[J]. Library and Information Service ,2013(12):136-142. [19] Chen Mingliang, Qiu Tingting, Xie Ying. Scientific construction of microblog's main influence evaluation index system[J]. Journal of Zhejiang University (Humanities and Social Sciences), 2014(02):53-63. [20] Cha M, Haddadi H, Benevenuto F, et al. Measuring user influence in Twitter: the million follower fallacy: International Conference on Weblogs and Social Media, Icwsm 2010, Washington, Dc, Usa, May,2010[C]. [21] Gao Chong, Yang Boxu. Analysis of content production mode of micro video-based on the research of Sina Weibo official short video application "Miaopai"[J]. Journalism and Mass Communication Monthly,2016(23):60-65. [22] Mao J X, Liu Y Q, Zhang M, et al. Social influence analysis for Microblog user based on user behavior[J]. Chinese Journal of Computers,2014. [23] Jun M A, Zhou G, Bin X U, et al. Analysis of user influence in Microblog based on individual attribute features[J]. Application Research of Computers,2013,30(8):2483-2487. [24] Boyd D, Golder S, Lotan G. Tweet, tweet, retweet: conversational aspects of retweeting on Twitter: Hawaii International Conference on System Sciences,2010[C]. [25] Jie L, Yun L, Zhen-jiang Z, Chang-ni G. Personalized recommendation via an improved NBI algorithm and user influence model in a Microblog network[J]. Physica A: Statistical Mechanics and its Applications. 2013, 392(19):4594-4605.