Abstract
Typically, case-based recommender systems recommend single items to the on-line customer. In this paper we introduce the idea of recommending a user-defined collection of items where the user has implicitly encoded the relationships between the items. Automated collaborative filtering (ACF), a socalled ‘contentless’ technique, has been widely used as a recommendation strategy for music items. However, its reliance on a global model of the user’s interests makes it unsuited to catering for the user’s local interests. We consider the context-sensitive task of building a compilation, a user-defined collection of music tracks. In our analysis, a collection is a case that captures a specific shortterm information/music need. In an offline evaluation, we demonstrate how a case-completion strategy that uses short-term representations is significantly more effective than the ACF technique. We then consider the problem of recommending a compilation according to the user’s most recent listening preferences. Using a novel on-line evaluation where two algorithms compete for the user’s attention, we demonstrate how a knowledge-light case-based reasoning strategy successfully addresses this problem.
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Hayes, C., Avesani, P., Baldo, E., Cunningham, P. (2005). Re-using Implicit Knowledge in Short-Term Information Profiles for Context-Sensitive Tasks. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_25
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DOI: https://doi.org/10.1007/11536406_25
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