The topic-perspective model for social tagging systems

In this paper, we propose a new probabilistic generative model, called Topic-Perspective Model, for simulating the generation process of social annotations. Different from other generative models, in our model, the tag generation process is separated from the content term generation process. While content terms are only generated from resource topics, social tags are generated by resource topics and user perspectives together. The proposed probabilistic model can produce more useful information than any other models proposed before. The parameters learned from this model include: (1) the topical distribution of each document, (2) the perspective distribution of each user, (3) the word distribution of each topic, (4) the tag distribution of each topic, (5) the tag distribution of each user perspective, (6) and the probabilistic of each tag being generated from resource topics or user perspectives. Experimental results show that the proposed model has better generalization performance or tag prediction ability than other two models proposed in previous research.

[1]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[2]  Tom Minka,et al.  Expectation-Propogation for the Generative Aspect Model , 2002, UAI.

[3]  Michael I. Jordan,et al.  Modeling annotated data , 2003, SIGIR.

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

[5]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[6]  J. Lafferty,et al.  Mixed-membership models of scientific publications , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[8]  Peter Mika,et al.  Ontologies are us: A unified model of social networks and semantics , 2005, J. Web Semant..

[9]  John Riedl,et al.  tagging, communities, vocabulary, evolution , 2006, CSCW '06.

[10]  Yong Yu,et al.  Exploring social annotations for the semantic web , 2006, WWW '06.

[11]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[12]  R. Lambiotte,et al.  Collaborative Tagging as a Tripartite Network , 2005, International Conference on Computational Science.

[13]  Padhraic Smyth,et al.  Statistical entity-topic models , 2006, KDD '06.

[14]  Andreas Hotho,et al.  Tag Recommendations in Folksonomies , 2007, LWA.

[15]  Kristina Lerman,et al.  Exploiting Social Annotation for Automatic Resource Discovery , 2007, ArXiv.

[16]  Vittorio Loreto,et al.  Network properties of folksonomies , 2007, AI Commun..

[17]  Hongyuan Zha,et al.  Exploring social annotations for information retrieval , 2008, WWW.

[18]  Yang Song,et al.  A sparse gaussian processes classification framework for fast tag suggestions , 2008, CIKM '08.

[19]  Hector Garcia-Molina,et al.  Social tag prediction , 2008, SIGIR '08.

[20]  Wolfgang Nejdl,et al.  Can all tags be used for search? , 2008, CIKM '08.

[21]  Georgia Koutrika,et al.  Can social bookmarking improve web search? , 2008, WSDM '08.

[22]  Hector Garcia-Molina,et al.  Clustering the tagged web , 2009, WSDM '09.

[23]  Xin Chen,et al.  Exploit the tripartite network of social tagging for web clustering , 2009, CIKM.

[24]  Hans-Peter Kriegel,et al.  Hierarchical Bayesian Models for Collaborative Tagging Systems , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[25]  Xin Chen,et al.  Probabilistic models for topic learning from images and captions in online biomedical literatures , 2009, CIKM.

[26]  Said Kashoob,et al.  A Categorical Model for Discovering Latent Structure in Social Annotations , 2009, ICWSM.

[27]  Rui Li,et al.  Exploring social tagging graph for web object classification , 2009, KDD.

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