Clustering-based diversity improvement in top-N recommendation
暂无分享,去创建一个
[1] Daniel T. Larose,et al. Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .
[2] Barry Smyth,et al. Similarity vs. Diversity , 2001, ICCBR.
[3] Jun Wang,et al. Workshop on novelty and diversity in recommender systems - DiveRS 2011 , 2011, RecSys '11.
[4] Greg Linden,et al. Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .
[5] Tova Milo,et al. Diversification and refinement in collaborative filtering recommender , 2011, CIKM '11.
[6] George Karypis,et al. A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.
[7] Hyung Jun Ahn,et al. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..
[8] Sean M. McNee,et al. Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.
[9] Neil J. Hurley,et al. Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation , 2011, TOIT.
[10] Roberto Turrin,et al. Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.
[11] Jonathan L. Herlocker,et al. Evaluating collaborative filtering recommender systems , 2004, TOIS.
[12] Barry Smyth,et al. Improving Recommendation Diversity , 2001 .
[13] Michael J. Pazzani,et al. Learning Collaborative Information Filters , 1998, ICML.
[14] Jennifer Golbeck,et al. Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.
[15] Neil J. Hurley,et al. Novel Item Recommendation by User Profile Partitioning , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.
[16] Vipin Kumar,et al. Introduction to Data Mining, (First Edition) , 2005 .
[17] 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.
[18] George Karypis,et al. Item-based top-N recommendation algorithms , 2004, TOIS.
[19] Qingsheng Zhu,et al. Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization , 2012, Knowl. Based Syst..
[20] Arbee L. P. Chen,et al. A Music Recommendation System Based on Music and User Grouping , 2005, Journal of Intelligent Information Systems.
[21] David Heckerman,et al. Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.
[22] Gediminas Adomavicius,et al. Maximizing aggregate recommendation diversity , 2011, RecSys 2011.
[23] Gediminas Adomavicius,et al. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques , 2012, IEEE Transactions on Knowledge and Data Engineering.
[24] Antonio Hernando,et al. Collaborative filtering adapted to recommender systems of e-learning , 2009, Knowl. Based Syst..
[25] Yehuda Koren,et al. Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.
[26] David C. Yen,et al. An implementation and evaluation of recommender systems for traveling abroad , 2011, Expert Syst. Appl..
[27] Bradley N. Miller,et al. GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.
[28] Diarmuid O'Donoghue. Proceedings of 12th Irish Conference on Artificial Intelligence & Cognitive Science (AICS - 2001) , 2001 .
[29] Sreenivas Gollapudi,et al. An axiomatic approach for result diversification , 2009, WWW '09.
[30] Giuseppe M. L. Sarnè,et al. A multi-agent recommender system for supporting device adaptivity in e-Commerce , 2011, Journal of Intelligent Information Systems.
[31] Sean M. McNee,et al. Improving recommendation lists through topic diversification , 2005, WWW '05.
[32] Bracha Shapira,et al. Recommender Systems Handbook , 2015, Springer US.
[33] Mi Zhang,et al. Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.
[34] Yehuda Koren,et al. Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.