An Analysis of Memory Based Collaborative Filtering Recommender Systems with Improvement Proposals

Memory Based Collaborative Filtering Recommender Systems have been around for the best part of the last twenty years. It is a mature technology, implemented in nu- merous commercial applications. However, a departure from Memory Based systems, in favour of Model Based systems happened during the last years. The Net ix.com competition of 2006, brought the Model Based paradigm to the spotlight, with plenty of research that followed. Still, these matrix factorization based algorithms are hard to compute, and cumbersome to update. Memory Based approaches, on the other hand, are simple, fast, and self explanatory. We posit that there are still uncomplicated approaches that can be applied to improve this family of Recommender Systems further. Four strategies aimed at improving the Accuracy of Memory Based Collaborative Filtering Recommender Systems have been proposed and extensively tested. The strategies put forward include an Average Item Voting approach to infer missing rat- ings, an Indirect Estimation algorithm which pre-estimates the missing ratings before computing the overall recommendation, a Class Type Grouping strategy to lter out items of a class di erent than the target one, and a Weighted Ensemble consisting of an average of an estimation computed with all samples, with one obtained via the Class Type Grouping approach. This work will show that there is still ample space to improve Memory Based Systems, and raise their Accuracy to the point where they can compete with state- of-the-art Model Based approaches such as Matrix Factorization or Singular Value Decomposition techniques, which require considerable processing power, and generate models that become obsolete as soon as users add new ratings into the system.

[1]  Neil Yorke-Smith,et al.  A Novel Bayesian Similarity Measure for Recommender Systems , 2013, IJCAI.

[2]  Ke Wang,et al.  RecTree: An Efficient Collaborative Filtering Method , 2001, DaWaK.

[3]  Bin Li,et al.  Cross-Domain Collaborative Filtering: A Brief Survey , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[4]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[5]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

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

[7]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[8]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[9]  YiBo Huang.  An item based collaborative filtering using item clustering prediction , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

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

[11]  Yehuda Koren,et al.  The BellKor Solution to the Netflix Grand Prize , 2009 .

[12]  Liang Zhang,et al.  MODELING ITEM-ITEM SIMILARITIES FOR PERSONALIZED RECOMMENDATIONS ON YAHOO! FRONT PAGE , 2011, 1111.0416.

[13]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[14]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

[15]  Jure Leskovec,et al.  Mining of Massive Datasets: Finding Similar Items , 2011 .

[16]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[17]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[18]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[19]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[20]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[21]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[22]  K. Nageswara Rao,et al.  Application Domain and Functional Classification of Recommender Systems—A Survey , 2008 .

[23]  Richard S. Zemel,et al.  Collaborative Filtering and the Missing at Random Assumption , 2007, UAI.

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

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

[26]  Marc Boullé,et al.  Comparing State-of-the-Art Collaborative Filtering Systems , 2007, MLDM.

[27]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[28]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[29]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[30]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[31]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[32]  Lalita Sharma,et al.  A Survey of Recommendation System: Research Challenges , 2013 .

[33]  Meenakshi Sharma,et al.  A Survey of Recommender Systems: Approaches and Limitations , 2013 .

[34]  Bamshad Mobasher,et al.  A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms , 2008, IEEE Data Eng. Bull..

[35]  A. Felfernig,et al.  A Short Survey of Recommendation Technologies in Travel and Tourism , 2006 .

[36]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[37]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[38]  Panagiotis Adamopoulos,et al.  Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems , 2013, RecSys.

[39]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[40]  Yehuda Koren,et al.  Improved Neighborhood-based Collaborative Filtering , 2007 .

[41]  Yehuda Koren,et al.  All Together Now: A Perspective on the Netflix Prize , 2010 .

[42]  Murphy J. Stephen,et al.  You Might Also Like , 2014 .

[43]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[44]  Sean Owen,et al.  Collaborative Filtering with Apache Mahout , 2012 .

[45]  Vitaly Shmatikov,et al.  2011 IEEE Symposium on Security and Privacy “You Might Also Like:” Privacy Risks of Collaborative Filtering , 2022 .

[46]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[47]  Namitha Ann Regi,et al.  A Survey on Recommendation Techniques in E-Commerce , 2013 .

[48]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[49]  Sahin Albayrak,et al.  Assessing the value of unrated items in collaborative filtering , 2007, 2007 2nd International Conference on Digital Information Management.

[50]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[51]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[52]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..