Abstract
Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding of these applications and their capabilities. Evaluation metrics continue to be refined but we still need to account for the relative contributions of the various knowledge elements that play a part in the recommendation process. In this paper, we make a fine-grained analysis of a successful case-based recommendation approach, providing an ablation study of similarity knowledge and similarity metric contributions to improved system performance. In particular, we extend our earlier analyses to examine how measures of interestingness can be used to identify and analyse relative contributions of segments of similarity knowledge. We gauge the strengths and weaknesses of knowledge components and discuss future work as well as implications for research in the area.
The support of the Informatics Research Initiative of Enterprise Ireland is gratefully acknowledged.
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O’Sullivan, D., Smyth, B., Wilson, D.C. (2004). Analysing Similarity Essence for Case Based Recommendation. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_52
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DOI: https://doi.org/10.1007/978-3-540-28631-8_52
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