Abstract
Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative Filtering (CF) is a succesful recommendation technique. User ratings are not expected to be independent, as users follow trends of similar rating behavior. In terms of Text Mining, this is analogous to the formation of higher-level concepts from plain terms. In this paper, we propose a novel CF algorithm which uses Latent Semantic Indexing (LSI) to detect rating trends and performs recommendations according to them. Our results indicate its superiority over existing CF algorithms.
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© 2007 Springer-Verlag Berlin Heidelberg
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Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y. (2007). Collaborative Filtering Based on User Trends. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_42
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DOI: https://doi.org/10.1007/978-3-540-70981-7_42
Publisher Name: Springer, Berlin, Heidelberg
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