Combining Case-Based and Similarity-Based Product Recommendation

Product recommender systems are a popular application and research field of CBR for several years now. However, almost all CBRbased recommender systems are not case-based in the original view of CBR, but just perform a similarity-based retrieval of product descriptions. Here, a predefined similarity measure is used as a heuristic for estimating the customers’ product preferences. In this paper we propose an extension of these systems, which enables case-based learning of customer preferences. Further, we show how this approach can be combined with existing approaches for learning the similarity measure directly. The presented results of a first experimental evaluation demonstrate the feasibility of our novel approach in an example test domain.