Tensor methods and recommender systems

Evgeny Frolov, Ivan Oseledets

Research output: Contribution to journalReview articlepeer-review

53 Citations (Scopus)

Abstract

A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nature of real environments, which allows to produce more accurate, situational (e.g., context-aware and criteria-driven) recommendations. Despite the promising results, tensor-based methods are poorly covered in existing recommender systems surveys. This survey aims to complement previous works and provide a comprehensive overview on the subject. To the best of our knowledge, this is the first attempt to consolidate studies from various application domains, which helps to get a notion of the current state of the field. We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems. WIREs Data Mining Knowl Discov 2017, 7:e1201. doi: 10.1002/widm.1201. For further resources related to this article, please visit the WIREs website.

Original languageEnglish
Article numbere1201
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume7
Issue number3
DOIs
Publication statusPublished - 1 May 2017

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