Abstract
Most recommendation systems employ variations of Collaborative Filtering (CF) for formulating suggestions of items relevant to users’ interests. However, CF requires expensive computations that grow polynomially with the number of users and items in the database. Methods proposed for handling this scalability problem and speeding up recommendation formulation are based on approximation mechanisms and, even when performance improves, they most of the time result in accuracy degradation. We propose a method for addressing the scalability problem based on incremental updates of user-to-user similarities. Our Incremental Collaborative Filtering (ICF) algorithm (i) is not based on any approximation method and gives the potential for high-quality recommendation formulation (ii) provides recommendations orders of magnitude faster than classic CF and thus, is suitable for online application.
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Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E. (2005). Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_57
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DOI: https://doi.org/10.1007/11425274_57
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