Recommending Users and Communities in Social Media

Social media has become increasingly prevalent in the last few years, not only enabling people to connect with each other by social links, but also providing platforms for people to share information and interact over diverse topics. Rich user-generated information, for example, users’ relationships and daily posts, are often available in most social media service websites. Given such information, a challenging problem is to provide reasonable user and community recommendation for a target user, and consequently, help the target user engage in the daily discussions and activities with his/her friends or like-minded people. In this article, we propose a unified framework of recommending users and communities that utilizes the information in social media. Given a user’s profile or a set of keywords as input, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user or keywords. With the proposed framework, users can find other individuals or communities sharing similar interests, and then have more interaction with these users or within the communities. We present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topical interests of users, as well as the focuses of communities. Extensive experimental evaluation and case studies on a dataset collected from Twitter demonstrate the effectiveness of our proposed framework compared with other probabilistic-topic-model-based recommendation methods.

[1]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[2]  Hongyuan Zha,et al.  Probabilistic models for discovering e-communities , 2006, WWW '06.

[3]  Wei Peng,et al.  FRec: a novel framework of recommending users and communities in social media , 2013, CIKM.

[4]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[5]  Thomas Hofmann,et al.  Semi-supervised Learning on Directed Graphs , 2004, NIPS.

[6]  Fei Wang,et al.  Community discovery using nonnegative matrix factorization , 2011, Data Mining and Knowledge Discovery.

[7]  Steve Gregory,et al.  Finding missing edges and communities in incomplete networks , 2011, ArXiv.

[8]  Susan T. Dumais,et al.  Characterizing Microblogs with Topic Models , 2010, ICWSM.

[9]  Edward Y. Chang,et al.  Collaborative filtering for orkut communities: discovery of user latent behavior , 2009, WWW '09.

[10]  Wei Peng,et al.  Temporal relation co-clustering on directional social network and author-topic evolution , 2011, Knowledge and Information Systems.

[11]  Gregor Heinrich.  Parameter estimation for text analysis , 2009 .

[12]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[13]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[14]  Yossi Matias,et al.  Suggesting friends using the implicit social graph , 2010, KDD.

[15]  John Yen,et al.  An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks , 2007, 2007 IEEE Intelligence and Security Informatics.

[16]  Jian Xu,et al.  Social network user influence sense-making and dynamics prediction , 2014, Expert Syst. Appl..

[17]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[18]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[19]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[21]  Lyle H. Ungar,et al.  Statistical Relational Learning for Link Prediction , 2003 .

[22]  Yee Whye Teh,et al.  On Smoothing and Inference for Topic Models , 2009, UAI.

[23]  Ben Taskar,et al.  Link Prediction in Relational Data , 2003, NIPS.

[24]  Yun Chi,et al.  Combining link and content for community detection: a discriminative approach , 2009, KDD.

[25]  Marco Pennacchiotti,et al.  Investigating topic models for social media user recommendation , 2011, WWW.

[26]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[27]  Thomas L. Griffiths,et al.  Learning author-topic models from text corpora , 2010, TOIS.

[28]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[29]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[30]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[31]  Jiawei Han,et al.  Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling , 2012, TIST.

[32]  Padhraic Smyth,et al.  Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model , 2006, NIPS.

[33]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[34]  L. Venkata Subramaniam,et al.  Using content and interactions for discovering communities in social networks , 2012, WWW.

[35]  Volker Tresp,et al.  Soft Clustering on Graphs , 2005, NIPS.

[36]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[37]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[38]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[39]  Deng Cai,et al.  Topic modeling with network regularization , 2008, WWW.

[40]  Edward Y. Chang,et al.  Combinational collaborative filtering for personalized community recommendation , 2008, KDD.

[41]  Yan Liu,et al.  Topic-link LDA: joint models of topic and author community , 2009, ICML '09.

[42]  Jiawei Han,et al.  ACM Transactions on Knowledge Discovery from Data: Introduction , 2007 .

[43]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[44]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.