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An Adaptive Personalized Recommendation Strategy Featuring Context Sensitive Content Adaptation Zeina Chedrawy1 and Syed Sibte Raza Abidi1 1 Faculty of Computer Science, Dalhousie University, Halifax B3H 1W5, Canada {chedrawy, sraza}@cs.dal.ca Abstract. In this paper, we present a new approach that is a synergy of item- based Collaborative Filtering (CF) and Case Based Reasoning (CBR) for per- sonalized recommendations. We present a two-phase strategy: in phase I, we developed a context-sensitive item-based CF method that leverages the original past recommendations of peers via ratings performed on various information items. In phase II, we further personalize the information items comprising multiple components using a CBR-based compositional adaptation technique to selectively collect the most relevant information components and combine them into one composite recommendation. In this way, our approach allows fine- grained information filtering by operating at the constituent elements of an in- formation item as opposed to the entire information item. We show that our strategy improves the quality and relevancy of the recommendations in terms of its appropriateness to the user’s needs and interests, and validated by statistical significance tests. We demonstrate the working of our strategy by recommend- ing personalized music playlists. 1 Introduction The volume of information over the Internet is increasing at a tremendous rate, and as a consequence the search for ‘relevant’ and ‘useful’ information is becoming propor- tionally difficult. Adaptive recommender systems—a class of adaptive hypermedia systems—act as mediators between information sources and information seekers [6], as they exploit the user’s current specific interests and needs to (a) regulate the flow of information to users; and (b) direct users to the right information—i.e. personalized information selection and filtering [3]. Adaptive recommender systems are applied in a va