A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

[1]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[2]  Mario Cataldi,et al.  Emerging topic detection on Twitter based on temporal and social terms evaluation , 2010, MDMKDD '10.

[3]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[4]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[5]  Yujie Zhang,et al.  Mobile Recommender Systems and Their Applications: Mobile Recommender Systems and Their Applications , 2014 .

[6]  Qinying Liao,et al.  Smarter social collaboration at IBM research , 2011, CSCW '11.

[7]  Jia Zhang,et al.  A Social Tagging Based Collaborative Filtering Recommendation Algorithm for Digital Library , 2011, ICADL.

[8]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[9]  Kristina Lerman,et al.  Social Tagging , 2014, Encyclopedia of Social Network Analysis and Mining.

[10]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[11]  Chris H. Q. Ding,et al.  Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence Chi-Square Statistic, and a Hybrid Method , 2006, AAAI.

[12]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[13]  Yong Yu,et al.  Collaborative personalized tweet recommendation , 2012, SIGIR '12.

[14]  Meng Xiang.  Mobile Recommender Systems and Their Applications , 2013 .

[15]  Analía Amandi,et al.  Followee recommendation based on text analysis of micro-blogging activity , 2013, Inf. Syst..

[16]  David Carmel,et al.  Social media recommendation based on people and tags , 2010, SIGIR.

[17]  Amitabha Bagchi,et al.  Temporal Analysis of User Behavior and Topic Evolution on Twitter , 2013, BDA.

[18]  Xing Chunxiao,et al.  A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change , 2007 .

[19]  Weigang Li,et al.  Querying dynamic communities in online social networks , 2014, Journal of Zhejiang University SCIENCE C.

[20]  Deng Ai,et al.  A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction , 2003 .

[21]  Hae-Chang Rim,et al.  Identifying interesting Twitter contents using topical analysis , 2014, Expert Syst. Appl..

[22]  Ling Guan,et al.  A hybrid approach for personalized recommendation of news on the Web , 2012, Expert Syst. Appl..

[23]  Du Xiaoyong,et al.  Recommendation algorithm combining the user-based classified regression and the item-based filtering , 2006 .

[24]  Kai Yu,et al.  Temporal Social Tagging Based Collaborative Filtering Recommender for Digital Library , 2012, ICADL.

[25]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[26]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[27]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[28]  Byeong Man Kim,et al.  A new approach for combining content-based and collaborative filters , 2003, Journal of Intelligent Information Systems.

[29]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Shuang-Hong Yang,et al.  Functional matrix factorizations for cold-start recommendation , 2011, SIGIR.