Network-based recommendation algorithms: A review

Fei Yu, An Zeng, Sébastien Gillard and Matúš Medo

Physica A: Statistical Mechanics and its Applications, 2016, vol. 452, issue C, 192-208

Abstract: Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users’ past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use–such as the possible influence of recommendation on the evolution of systems that use it–and finally discuss open research directions and challenges.

Keywords: Information filtering; Recommender systems; Complex networks; Random walk (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:452:y:2016:i:c:p:192-208

DOI: 10.1016/j.physa.2016.02.021

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