@inproceedings{pub2024,
    abstract = {Product recommender systems are a popular application and research field of CBR for several years now. However, almost all CBR-based 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 heuristics for estimating the customers' product preferences. In this paper we propose an extension of these systems, which enables case-based learning of customer preferences and which also allows to incorporate collaborative recommendation techniques. 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 exemplary test domain.},
    year = {2006},
    title = {Combining Case-Based and Similarity-Based Product Recommendation},
    booktitle = {Proceedings of the 8th European Conference on Case-Based Reasoning (ECCBR 2006).},
    publisher = {Springer},
    author = {Armin Stahl},
    url = {http://www.dfki.de/web/kompetenz/vof/publikationen/renameFileForDownload?filename=ECCBR2006_Stahl.pdf&file_id=uploads_486 http://www.dfki.de/web/kompetenz/vof/publikationen/renameFileForDownload?filename=ECCBR06_Stahl_Slides.pdf&file_id=uploads_487 http://www.springerlink.com/content/425486w62h11l027/}
}