In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely adopted strategy today 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 (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost. These characteristics allow LCBM to efficiently handle large instances of the collaborative filtering problem on a single machine in short timeframes.

LCBM: A fast and lightweight collaborative filtering algorithm for binary ratings / Petroni, Fabio; Querzoni, Leonardo; Beraldi, Roberto; Paolucci, M.. - In: THE JOURNAL OF SYSTEMS AND SOFTWARE. - ISSN 0164-1212. - STAMPA. - 117:(2016), pp. 583-594. [10.1016/j.jss.2016.04.062]

LCBM: A fast and lightweight collaborative filtering algorithm for binary ratings

PETRONI, FABIO
;
QUERZONI, Leonardo
;
BERALDI, ROBERTO
;
2016

Abstract

In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely adopted strategy today 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 (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost. These characteristics allow LCBM to efficiently handle large instances of the collaborative filtering problem on a single machine in short timeframes.
2016
Big data; Collaborative filtering; Personalization; Recommendation systems; Hardware and Architecture; Software; Information Systems
01 Pubblicazione su rivista::01a Articolo in rivista
LCBM: A fast and lightweight collaborative filtering algorithm for binary ratings / Petroni, Fabio; Querzoni, Leonardo; Beraldi, Roberto; Paolucci, M.. - In: THE JOURNAL OF SYSTEMS AND SOFTWARE. - ISSN 0164-1212. - STAMPA. - 117:(2016), pp. 583-594. [10.1016/j.jss.2016.04.062]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/886415
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