Using Collaborative Filtering Data in Case-based Recommendation

In the context of PTV, an applied recommender system operating in the TV listings domain, we are examining the potential benefits in merging case-based and collab-orative filtering (CF) recommendation techniques by developing case-based reasoning (CBR) methods that employ collaborative filtering style ratings profiles di-rectly as cases. Doing so presents a number of chal-lenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering sys-tems. This paper expands on earlier CBR views of collaborative filtering, identifies problems and opportu-nities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge