Abstract
Case-based reasoning systems usually accept the conventional similarity assumption during retrieval, preferring to retrieve a set of cases that are maximally similar to the target problem. While we accept that this works well in many domains, we suggest that in others it is misplaced. In particular, we argue that often diversity can be as important as similarity. This is especially true in case-based recommender systems. In this paper we propose and evaluate strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency.
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Smyth, B., McClave, P. (2001). Similarity vs. Diversity. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_25
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DOI: https://doi.org/10.1007/3-540-44593-5_25
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