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On Distributing Bayesian Personalized Ranking from Implicit Feedback

Pairwise learning is a popular technique for collaborative ranking with implicit, positive only feedback. Bayesian Personalized Ranking (BPR) was recently proposed for this task and its ranking is among the bests. Because its learning is based on stochastic gradient descent (SGD) with uniformly drawn pairs, it converges slowly especially in the case of a very large pool of items. We propose an approach to distribute its computation in order to face its scalability issue.

Auteur(s) : Proceedings of CARI 2016
Pages : 117 – 125
Année de publication : 2016
Revue : Colloque Africain sur la Recherche en Informatique
N° de volume : CARI 2016
Type : Article
Mise en ligne par : GUEYE Modou