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Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Department of Informatics & Telematics, Harokopio University of Athens, 176 76 Athens, Greece
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Information 2019, 10(5), 155; https://doi.org/10.3390/info10050155
Received: 24 March 2019 / Revised: 21 April 2019 / Accepted: 25 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Modern Recommender Systems: Approaches, Challenges and Applications)
Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark. View Full-Text
Keywords: recommender systems; collaborative filtering; scalability; graph partitioning; distributed systems; parallel execution; social networks recommender systems; collaborative filtering; scalability; graph partitioning; distributed systems; parallel execution; social networks
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Sardianos, C.; Ballas Papadatos, G.; Varlamis, I. Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance. Information 2019, 10, 155.

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