A Movie Recommendation System ^ An Application of Voting Theory in User Modeling RAJATISH MUKHERJEE, NEELIMA SAJJA and SANDIP SEN Mathematical & Computer Sciences Department, University of Tulsa e-mail: {rajatish,sajjane}@ens.utulsa.edu, sandip@kolkata.mcs.utulsa.edu (Received: 15 October 2000; accepted in revised form: 30 August 2001) Abstract. Our research agenda focuses on building software agents that can employ user mod- eling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly bene¢cial application of intelligent agent technology. A particular kind of assis- tant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeo¡ between user preferences.We have adapted mecha- nisms from voting theory that have desirable guarantees regarding the recommendations gen- erated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users.We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback about its recommended movies from the user.We also provide pro-active information gather- ing to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and e¡ective recommendations. Key words. pro-active information gathering, recommender system, text-based learning, user modeling, voting theory 1. Introduction Research on intelligent information agents has recently attracted much attention. As the amount of information on the Internet grows at an astonishing speed, an average user feels overwhelmed navigating through today’s information highway. Information overload and automation of electronic markets are today’s relevant problems.We often need to obtain and re¢ne information from the Internet (a huge and often unstructured source of information) to achieve our goals.There has been e¡orts to automate ¢ltering of relevant information and presenting organized information (the summary) to the user. Such automated methods, commonly referred to as intelligent information retrieval are used to locate and retrieve information with respect to a user’s individual preferences (Balabanovic, 1998; Lang, 1995; Maglio and Barett, 1997; Pazzani and User Modeling and User-Adapted Interaction 13: 5^33, 2003. 5 # 2003 Kluwer Academic Publishers. Printed in the Netherlands.