Abstract
In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today’s a widely adopted strategy to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost.
This work has been partially supported by the TENACE PRIN Project (n. 20103P34XC) funded by the Italian Ministry of Education, University and Research.
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Petroni, F., Querzoni, L., Beraldi, R., Paolucci, M. (2014). LCBM: Statistics-Based Parallel Collaborative Filtering. In: Abramowicz, W., Kokkinaki, A. (eds) Business Information Systems. BIS 2014. Lecture Notes in Business Information Processing, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-319-06695-0_15
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DOI: https://doi.org/10.1007/978-3-319-06695-0_15
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