This thesis provides a novel approach to using data mining for e-commerce. The focus of our work is to apply association rule mining to collaborative recommender systems, which recommend articles to a user on the basis of other users' ratings for these articles as well as the similarities between this user's and other users' tastes. In this work, we propose a new algorithm for association rule mining specially tailored for use in collaborative recommendation. We make recommendations by exploring associations between users, associations between articles, and a combination of the two. We experimentally evaluated our approach on real data for many different parameter settings and compared its performance with that of other approaches under similar experimental conditions. Through our analysis and experiments, we have found that association rules are quite appropriate for collaborative recommendation domains and that they can achieve a performance that is comparable to current state of the art in recommender systems research.
Worcester Polytechnic Institute
All authors have granted to WPI a nonexclusive royalty-free license to distribute copies of the work. Copyright is held by the author or authors, with all rights reserved, unless otherwise noted. If you have any questions, please contact firstname.lastname@example.org.
Lin, Weiyang, "Association Rule Mining for Collaborative Recommender Systems" (2000). Masters Theses (All Theses, All Years). 824.
Data Mining, Association Rules, Electronic Commerce, Collaborative Recommender Systems, Association marketing, Internet marketing, Electronic commerce, Recommender systems (Computer science)