Including Context in a Transactional Recommender System Using a Pre-filtering Approach: Two Real E-commerce Applications

Recent research has shown that including context in a recommender system may improve its performance. The context-based recommendation approaches are classified as pre-filtering, post-filtering and contextual modeling. Moreover, in real e-commerce applications, collecting ratings may be quite difficult. It is possible to use purchasing frequencies instead of ratings, but little research has been done. The research contribution of this work lies in studying when and how including context with a pre-filtering approach improves the performance of a recommender system using transactional data. To this aim, we studied the interaction between homogeneity and sparsity, in several experimental settings. The experiments were done on two databases coming from two actual e-commerce applications.

[1]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

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

[3]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[4]  Alexander Tuzhilin,et al.  Using Context to Improve Predictive Modeling of Customers in Personalization Applications , 2008, IEEE Transactions on Knowledge and Data Engineering.

[5]  Bamshad Mobasher,et al.  Intelligent Techniques for Web Personalization , 2005, Lecture Notes in Computer Science.

[6]  Gediminas Adomavicius,et al.  Multidimensional Recommender Systems: A Data Warehousing Approach , 2001, WELCOM.

[7]  Michael J. Pazzani,et al.  Syskill & Webert: Identifying Interesting Web Sites , 1996, AAAI/IAAI, Vol. 1.

[8]  M. F. Luce,et al.  Constructive Consumer Choice Processes , 1998 .

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

[10]  Dunja Mladenic,et al.  From Web to Social Web: Discovering and Deploying User and Content Profiles, Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006. Revised Selected and Invited Papers , 2007, WebMine.

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

[12]  David M. Nichols,et al.  Implicit Rating and Filtering , 1998 .

[13]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[14]  Anand V. Bodapati Recommendation Systems with Purchase Data , 2008 .

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

[16]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[17]  Raymond J. Mooney and Paul N. Bennett and Loriene Roy,et al.  Book Recommending Using Text Categorization with Extracted Information , 1998 .

[18]  Bamshad Mobasher,et al.  Contextual Recommendation , 2007, WebMine.

[19]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[20]  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.