In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet indus- try. Collaborative Filtering (CF) represents today’s a widely adopted strategy to build recommendation engines. The most advanced CF tech- niques (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.
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. © Springer International Publishing Switzerland 2014.
LCBM: Statistics-based parallel collaborative filtering / Petroni, Fabio; Querzoni, Leonardo; Beraldi, Roberto; Mario, Paolucci. - 176 LNBIP:(2014), pp. 172-184. (Intervento presentato al convegno 17th International Conference on Business Information Systems, BIS 2014 tenutosi a Larnaca nel 22 May 2014 through 23 May 2014) [10.1007/978-3-319-06695-0].
LCBM: Statistics-based parallel collaborative filtering
PETRONI, FABIO;QUERZONI, Leonardo;BERALDI, ROBERTO;
2014
Abstract
In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet indus- try. Collaborative Filtering (CF) represents today’s a widely adopted strategy to build recommendation engines. The most advanced CF tech- niques (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.File | Dimensione | Formato | |
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