Measures of Similarity in Memory-Based Collaborative Filtering Recommender System: A Comparison

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.

[1]  Panagiotis Symeonidis,et al.  Collaborative Filtering: Fallacies and Insights in Measuring Similarity , 2006 .

[2]  Georg Lausen,et al.  On exploiting classification taxonomies in recommender systems , 2008, AI Commun..

[3]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

[4]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[5]  Daniel Lemire,et al.  Slope One Predictors for Online Rating-Based Collaborative Filtering , 2007, SDM.

[6]  Young Park,et al.  A time-based approach to effective recommender systems using implicit feedback , 2008, Expert Syst. Appl..

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

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

[9]  Peter Funk,et al.  Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems , 2002, ECCBR.

[10]  Young U. Ryu,et al.  Personalized Recommendation over a Customer Network for Ubiquitous Shopping , 2009, IEEE Transactions on Services Computing.

[11]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[12]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[13]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[14]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[15]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

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

[17]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

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

[19]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[20]  Ville Ollikainen,et al.  A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data , 2015, Knowl. Based Syst..

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

[22]  Erik Duval,et al.  Dataset-driven research for improving recommender systems for learning , 2011, LAK.

[23]  Martin Spann,et al.  The Interplay Between Online Consumer Reviews and Recommender Systems: An Experimental Analysis , 2014, Int. J. Electron. Commer..

[24]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

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

[26]  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.