Distributed architecture for Emphasis Type="Itali

Authors: Vreixo Formoso Diego Fernández Fidel Cacheda Victor Carneiro

Publish Date: 2014/06/27

Volume: 18, Issue: 4, Pages: 997-1017

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Abstract

Collaborative filtering is one of the most popular recommendation techniques While the quality of the recommendations has been significantly improved in the last years most approaches present poor efficiency and scalability In this paper we study several factors that affect the performance of a kNearest Neighbors algorithm and we propose a distributed architecture that significantly improves both throughput and response time Two techniques for distributing recommender systems user and item partition were proposed and evaluated using that simulation model We have found that user partition is generally better with a faster response time and higher throughput

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