Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
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Hui Xiong | Enhong Chen | Qi Liu | Chris H. Q. Ding | Jian Chen | Jian Jhen Chen | C. Ding | Enhong Chen | Hui Xiong | Qi Liu | Jing Chen | Enhong Chen
[1] T. Landauer,et al. Indexing by Latent Semantic Analysis , 1990 .
[2] John Riedl,et al. GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.
[3] Thomas Hofmann,et al. Probabilistic Latent Semantic Analysis , 1999, UAI.
[4] Rajeev Motwani,et al. The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.
[5] John Riedl,et al. Application of Dimensionality Reduction in Recommender Systems , 2000 .
[6] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.
[7] David M. Pennock,et al. Methods and metrics for cold-start recommendations , 2002, SIGIR '02.
[8] Jennifer Widom,et al. Scaling personalized web search , 2003, WWW '03.
[9] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[10] Thomas Hofmann,et al. Latent semantic models for collaborative filtering , 2004, TOIS.
[11] Michael J. Pazzani,et al. User Modeling for Adaptive News Access , 2000, User Modeling and User-Adapted Interaction.
[12] Jonathan L. Herlocker,et al. Evaluating collaborative filtering recommender systems , 2004, TOIS.
[13] Hsinchun Chen,et al. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering , 2004, TOIS.
[14] Kenneth Y. Goldberg,et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.
[15] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[16] Dimitris Plexousakis,et al. Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.
[17] Xue Li,et al. Time weight collaborative filtering , 2005, CIKM '05.
[18] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.
[19] Sean M. McNee,et al. Improving recommendation lists through topic diversification , 2005, WWW '05.
[20] Ching-Yung Lin,et al. Personalized recommendation driven by information flow , 2006, SIGIR.
[21] Marco Gori,et al. A Random-Walk Based Scoring Algorithm Applied to Recommender Engines , 2006, WEBKDD.
[22] W. Bruce Croft,et al. LDA-based document models for ad-hoc retrieval , 2006, SIGIR.
[23] François Fouss,et al. Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.
[24] Thomas L. Griffiths,et al. Probabilistic Topic Models , 2007 .
[25] ChengXiang Zhai,et al. Automatic labeling of multinomial topic models , 2007, KDD '07.
[26] Dan Frankowski,et al. Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.
[27] Ruslan Salakhutdinov,et al. Probabilistic Matrix Factorization , 2007, NIPS.
[28] John D. Lafferty,et al. A correlated topic model of Science , 2007, 0708.3601.
[29] András A. Benczúr,et al. Methods for large scale SVD with missing values , 2007 .
[30] Stéphane Bressan,et al. A random walk on the red carpet: rating movies with user reviews and pagerank , 2008, CIKM '08.
[31] Panagiotis Symeonidis,et al. Providing Justifications in Recommender Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[32] Hanna Wallach,et al. Structured Topic Models for Language , 2008 .
[33] Mukkai S. Krishnamoorthy,et al. A random walk method for alleviating the sparsity problem in collaborative filtering , 2008, RecSys '08.
[34] Anh Duc Duong,et al. Addressing cold-start problem in recommendation systems , 2008, ICUIMC '08.
[35] Susumu Horiguchi,et al. Learning to classify short and sparse text & web with hidden topics from large-scale data collections , 2008, WWW.
[36] Hyung Jun Ahn,et al. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..
[37] Mi Zhang,et al. Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.
[38] Xiang Cheng,et al. Incremental probabilistic latent semantic analysis for automatic question recommendation , 2008, RecSys '08.
[39] Pabitra Mitra,et al. Feature weighting in content based recommendation system using social network analysis , 2008, WWW.
[40] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[41] Pasquale Lops,et al. Introducing Serendipity in a Content-Based Recommender System , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.
[42] Gediminas Adomavicius,et al. Context-aware recommender systems , 2008, RecSys '08.
[43] Yee Whye Teh,et al. On Smoothing and Inference for Topic Models , 2009, UAI.
[44] Ruslan Salakhutdinov,et al. Evaluation methods for topic models , 2009, ICML '09.
[45] Mi Zhang,et al. Enhancing diversity in Top-N recommendation , 2009, RecSys '09.
[46] Min Zhao,et al. Probabilistic latent preference analysis for collaborative filtering , 2009, CIKM.
[47] Thomas L. Griffiths,et al. Online Inference of Topics with Latent Dirichlet Allocation , 2009, AISTATS.
[48] Edward Y. Chang,et al. Collaborative filtering for orkut communities: discovery of user latent behavior , 2009, WWW '09.
[49] Yehuda Koren,et al. Collaborative filtering with temporal dynamics , 2009, KDD.
[50] John Riedl,et al. Tagommenders: connecting users to items through tags , 2009, WWW '09.
[51] Hui Xiong,et al. An energy-efficient mobile recommender system , 2010, KDD.
[52] Hui Xiong,et al. Exploiting user interests for collaborative filtering: interests expansion via personalized ranking , 2010, CIKM '10.
[53] T. Minka. Estimating a Dirichlet distribution , 2012 .