Case-Studies in Association Rule Mining for Recommender Systems Barry Smyth, Kevin McCarthy, James Reilly, Derry O’Sullivan and Lorraine McGinty Smart Media Institute, Department of Computer Science, University College Dublin (UCD), Dublin, Ireland barry.smyth@ucd.ie David C. Wilson Department of Software and Information Systems University Of North Carolina at Charlotte, USA davils@uncc.edu Abstract— Recommender systems combine ideas from in- formation retrieval, machine learning and user profiling re- search in order to provide end-users with more proactive and personalized information retrieval applications. Two popular approaches have come to dominate. Content-based techniques leverage the availability of rich item descriptions to identify new items that are similar to those that a user has liked in the past. In contrast, collaborative filtering techniques rely on the availability of user profiles in which sets of items have been rated. They recommend new items to a target user on the basis that similar users have preferred these items in the past. In this paper we will present two case- studies of how association rule mining techniques have been used to significantly enhance the power of content-based and collaborative filtering recommender systems. I. I NTRODUCTION We live in an age of information that provides for an unprecedented level of access to all forms of electronic data, products and services. However, users are finding it increasingly difficult to locate the right information at the right time and while Web search engines serve as our primary information access tool we are frequently frustrated by their inability to accurately capture our information retrieval needs [?], [?]. Over the past number of years the field of recom- mender systems has emerged with the stated goal of providing more proactive, intelligent and personalized information retrieval systems that are better able to cater for the needs of users in a variety of circumstances [?], [?]. Recommender systems combine ideas from artificial intelligence, machine learning, information retrieval and user profiling and have been applied successfully in a variety of domains including e-commerce and online shopping; see for e.g. [?], [?]. Over time two particular approaches to recommendation have emerged to domi- nate research and development. Content-based and case- based recommenders adopt a traditional information retrieval stance, relying on feature-based descriptions of information items as the basis for recommendation [?], [?], [?]. A typical content-based recommender se- lects items for recommendation on the grounds that they are similar to items that the user has liked in the past. In contrast, collaborative filtering approaches to recommendation adopt a very different standpoint, under the assumption that content-based descriptions may not be available. Instead they rely on ratings-based user profiles—profiles containing items that the user has previously rated according to their appeal or relevance. Recommendations are produced by locating users with similar ratings histories and by selecting items from these profiles that have been highly rated but that are absent from the target user’s profile [?], [?], [?]. In our research we have developed a wide range of recommender systems that include content-based and collaborative filtering aspects (e.g. [?], [?], [?], [?]). On their own the content-based and collaborative approaches suffer from a number of drawbacks, but taken together they can form the basis of a powerful hybrid recommen- dation strategy [?]. In recent years we have investigated how data-mining techniques may be able to improve the performance of content-based, collaborative and hybrid recommender systems [?], [?], [?]. In this paper we describe two case-studies from this work, where we have used association rule mining techniques to improve the performance of collaborative filtering and content-based recommenders. In Section II we describe how the well-