Exploring User Influence for Topical Group Recommendation

WANG Jing; ZHAO Hui; LIU Zhijing

doi:10.1049/cje.2016.11.017

Volume 26 Issue 1

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WANG Jing, ZHAO Hui, LIU Zhijing, “Exploring User Influence for Topical Group Recommendation,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 106-111, 2017, doi: 10.1049/cje.2016.11.017

Citation:

WANG Jing, ZHAO Hui, LIU Zhijing, “Exploring User Influence for Topical Group Recommendation,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 106-111, 2017, doi: 10.1049/cje.2016.11.017

WANG Jing, ZHAO Hui, LIU Zhijing, “Exploring User Influence for Topical Group Recommendation,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 106-111, 2017, doi: 10.1049/cje.2016.11.017

Citation:

WANG Jing, ZHAO Hui, LIU Zhijing, “Exploring User Influence for Topical Group Recommendation,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 106-111, 2017, doi: 10.1049/cje.2016.11.017

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Exploring User Influence for Topical Group Recommendation

doi: 10.1049/cje.2016.11.017

1.
Xidian University, Xi'an 710071, China;
2.
Xi'an Jiaotong University, Xi'an 710049, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61202177) and the Fundamental Research Funds for the Central Universities (No.7214572801).

More Information

Corresponding author: ZHAO Hui (corresponding author) was born in 1983. He received the B.E. and M.S. degrees in computer science and technology from Xidian University in 2005 and 2008. His research interests include recommendation system and personalized recommendation for m-learning. (Email:sheen.zhao@hotmail.com)
Received Date: 2014-12-11
Rev Recd Date: 2015-10-10
Publish Date: 2017-01-10

Abstract

Abstract

With the development of social networks, many recommendation systems recommend items to a group of users, known as group recommendation. However, it will not be an appropriate recommendation without user topical influence analysis. We proposed a new group recommendation based on user topical influence analysis. We firstly construct several topical sub-groups depending on topics. Then we analyze user topical influence in sub-group, including user influence on specific topic and the topical sub-group. Besides, four user-factors are introduced to calculate the user social influence on topical sub-groups more accurately. Based on user topical influence analysis, we present our topical group recommendation algorithm, which calculates the predicted rating value for sub-group by aggregating weighted ratings of all users in the sub-group. The experimental results show convincingly that our proposed method can improve the group recommendation quality.
- Group recommendation,
- Social networks,
- Topical sub-groups,
- User topical influence

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References(21)

References

S. Aral and D. Walker, "Identifying influential and susceptible members of social networks", Science, Vol.337, No.6092, pp.337-341, 2012.

Q. Gao, F. Abel, G.J. Houben, et al., "A comparative study of users' microblogging behavior on Sina Weibo and Twitter", Proc. of the 20th International Conference on User Modeling, Adaptation and Personalization, Montreal, QC, Canada, pp.88-101, 2012.

M. Trusov, A.V. Bodapati and R.E. Bucklin, "Determining influential users in internet social networks", Journal of Marketing Research, Vol.47, No.4, pp.643-658, 2010.

A. Goyal, F. Bonchi and L. Lakshmanan, "Learning influence probabilities in social networks", Proc. of the third ACM International Conference on Web Search and Data Mining, New York City, NY, USA, pp.241-250, 2010.

J.T. Sang and C.S. Xu, "Social influence analysis and application on multimedia sharing websites", ACM Transactions on Multimedia Computing, Communications and Applications, Vol.9, No.1s, Article ID 53, 24 pages, 2013.

T. Hofmann, "Probabilistic latent semantic indexing", Proc. of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, USA, pp.50-57, 1999.

D.M. Blei, A.Y. Ng and M.I. Jordan, "Latent dirichlet allocation", Journal of Machine Learning Research, Vol.3, pp.993-1022, 2003.

M. Steyvers, P. Smyth, M. Rosen-Zvi, et al., "Probabilistic author-topic models for information discovery", Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp.306-315, 2004.

D. Zhou, E. Manavoglu, J. Li, et al., "Probabilistic models for discovering e-communities", Proc. of the 15th International Conference on World Wide Web, Edinburgh, United Kingdom, pp.173-182, 2006.

A. McCallum, X. Wang and A. Corrada-Emmanuel, "Topic and role discovery in social networks with experiments on enron and academic email", Journal of Artificial Intelligence Research, Vol.30, pp.249-272, 2007.

X. Tang and C.C. Yang, "TUT:A statistical model for detecting trends, topics and user interests in social media", Proc. of the 21st ACM International Conference on Information and Knowledge Management, Maui, HI, USA, pp.972-981, 2012.

D.F. Li, X. Shuai, G.Z. Sun, et al., "Mining topic-level opinion influence in microblog", Proc. of the 21st ACM International Conference on Information and Knowledge Management, Maui, HI, USA, pp.1562-1566, 2012.

D. Correa, A. Sureka and M. Pundir, "iTop:Interaction based topic centric community discovery on twitter", Proc. of the 5th Ph.D. Workshop on Information and Knowledge, Maui, HI, USA, pp.51-58, 2012.

Y. Cha and J. Cho, "Social-network analysis using topic models", Proc. of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, Oregon, pp.565-574, 2012.

B. Sarwar, G. Karypis, J. Konstan, et al., "Item-based collaborative filtering recommendation algorithms", Proc. of 10th International Conference on World Wide Web, Hong Kong, pp.285-295, 2001.

S. Amer-Yahia, S.B. Roy, A. Chawlat, et al., "Group recommendation:Semantics and efficiency", Proceedings of the Very Large Database Endowment, Vol.2, No.1, pp.754-765, 2009.

S. Seko, T. Yagi, M. Motegi, et al., "Group recommendation using feature space representing behavioral tendency and power balance among members", Proc. of the 5th ACM Conference on Recommender Systems, Chicago, IL, USA, pp.101-108, 2011.

Q. Yuan, G. Cong and C.Y. Lin, "COM:A generative model for group recommendation", Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp.163-172, 2014.

X.J. Liu, Y. Tian, M. Ye, et al., "Exploring personal impact for group recommendation", Proc. of the 21st ACM International Conference on Information and Knowledge Management, Maui, HI, USA, pp.674-683, 2012.

J. Wang, Z.J. Liu and H. Zhao, "Micro-blogs entity recognition based on DSTCRF", Chinese Journal of Electronics, Vol.23, No.1, pp.147-150, 2014.

P. Soucy and G.W. Mineau, "A simple KNN algorithm for text categorization", Proc. of the 2001 IEEE International Conference on Data Mining, San Jose, CA, USA, pp.647-648, 2001.

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