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Summary: 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-
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