Exploiting Synergy between Ontologies and Recommender Systems Stuart E. Middleton Harith Alani. David C. De roure Intelligence, Agents and Multimedia Group Department of Electronics and Computer Science University of Southampton Southampton, So17 1BJ, UK (sem99r, ha, dder)@ecssoton ac uk ABSTRACT Recommender systems [23 learn about user preferences over time Recommender systems learn about user preferences over time and automatically find things of similar interest, thus reducing the automatically finding things of similar interest. This reduces the burden of creating explicit queries. They dynamically track users burden of creating explicit queries. Recommender systems do, as their interests change. However, such systems require an initial however, suffer from cold-start problems where no initial learning phase where behaviour information is built up to form an formation is available early on upon which to base user profile. During this initial learning phase performance is often poor due to the lack of user information; this is known as the cold-start problem [17] Semantic knowledge stru can provide valuable domain knowl Howeve There has been increasing interest in developing and using tools h kn not a trivial for creating annotated content and making it available over the task and user interests acquire and semantic web Ontologies are one such tool, used to maintain and provide access to specific knowledge repositories. Such source could complement the behavioral information held within This paper investigates the synergy between a web-based research recommender systems, by providing some initial knowledge about aper recommender system and an ontology containing users and their domains of interest. It should thus be possible to information automatically extracted from departmental databases available on the web. The ontology is used to address the bootstrap the initial learning phase of a recommender system with such knowledge, easing the cold-start problem. system addresses the ontology's interest-acquisition problem. An In return for any bootstrap information the recommender system performance of the integrated systems measured This would reduce the effort involved in acquiring and maintaining knowledge of people's research interests. To this end General terms we investigate the integration of Quickstep, a web-based Design, Experimentation recommender system, an ontology for the academic domain and OntoCoPl, a community of practice identifier that can pick out Keywords Cold-start problem, interest-acquisition problem, ontology, 2. RECOMMENDER SYSTEMS recommender system. People may find articulating what they want hard, but they are good at recognizing it when they see it. This insight has led to the 1. INTRODUCTION utilization of relevance feedback [24], where people rate web The mass of content available on the World-Wide Web raises pages as interesting or not interesting and the system tries to find important questions over its effective use. Search engines filter pages that match the interesting, positive examples and do not web pages that match explicit queries, but most people find match the not interesting, negative examples. With sufficient articulating exactly what they want difficult. The result is large positive and negative examples, modern machine learning lists of search results that contain a handful of useful pages, techniques can classify new pages with impressive accuracy. Such defeating the purpose of filtering in the first place systems are called content-based recommender systems Another way to recommend pages is based on the ratings of other people who have seen the page before. Collaborative Permission to make d ersonal or classroom use is granted c. ork for recommender systems do this by asking people to rate explicitly not made or distributed for profit and that pages and then recommending new pages that similar users have rated highly. The problem with collaborative filtering is that there otherwise, to republish, to post on servers or to redistribute to lists, is no direct reward for providing examples since they only help ecific permission by the authors other people. This leads to initial difficulties in obtaining a Semantic Web Workshop 2002 Hawaii, USA ufficient number of ratings for the system to be useful. Copyright by the authoExploiting Synergy between Ontologies and Recommender Systems Stuart E. Middleton, Harith Alani, David C. De Roure Intelligence, Agents and Multimedia Group Department of Electronics and Computer Science University of Southampton Southampton, SO17 1BJ, UK {sem99r,ha,dder}@ecs.soton.ac.uk ABSTRACT Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology’s interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured. General Terms Design, Experimentation. Keywords Cold-start problem, interest-acquisition problem, ontology, recommender system. 1. INTRODUCTION The mass of content available on the World-Wide Web raises important questions over its effective use. Search engines filter web pages that match explicit queries, but most people find articulating exactly what they want difficult. The result is large lists of search results that contain a handful of useful pages, defeating the purpose of filtering in the first place. Recommender systems [23] learn about user preferences over time and automatically find things of similar interest, thus reducing the burden of creating explicit queries. They dynamically track users as their interests change. However, such systems require an initial learning phase where behaviour information is built up to form an user profile. During this initial learning phase performance is often poor due to the lack of user information; this is known as the cold-start problem [17]. There has been increasing interest in developing and using tools for creating annotated content and making it available over the semantic web. Ontologies are one such tool, used to maintain and provide access to specific knowledge repositories. Such sources could complement the behavioral information held within recommender systems, by providing some initial knowledge about users and their domains of interest. It should thus be possible to bootstrap the initial learning phase of a recommender system with such knowledge, easing the cold-start problem. In return for any bootstrap information the recommender system could provide details of dynamic user interests to the ontology. This would reduce the effort involved in acquiring and maintaining knowledge of people’s research interests. To this end we investigate the integration of Quickstep, a web-based recommender system, an ontology for the academic domain and OntoCoPI, a community of practice identifier that can pick out similar users. 2. RECOMMENDER SYSTEMS People may find articulating what they want hard, but they are good at recognizing it when they see it. This insight has led to the utilization of relevance feedback [24], where people rate web pages as interesting or not interesting and the system tries to find pages that match the interesting, positive examples and do not match the not interesting, negative examples. With sufficient positive and negative examples, modern machine learning techniques can classify new pages with impressive accuracy. Such systems are called content-based recommender systems. Another way to recommend pages is based on the ratings of other people who have seen the page before. Collaborative recommender systems do this by asking people to rate explicitly pages and then recommending new pages that similar users have rated highly. The problem with collaborative filtering is that there is no direct reward for providing examples since they only help other people. This leads to initial difficulties in obtaining a sufficient number of ratings for the system to be useful. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission by the authors. Semantic Web Workshop 2002 Hawaii, USA Copyright by the authors