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Tensor methods and recommender systems
Author(s) -
Frolov Evgeny,
Oseledets Ivan
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1201
Subject(s) - recommender system , computer science , field (mathematics) , tensor (intrinsic definition) , context (archaeology) , complement (music) , data science , collaborative filtering , representation (politics) , situational ethics , information retrieval , artificial intelligence , mathematics , epistemology , philosophy , complementation , biology , politics , gene , political science , pure mathematics , law , phenotype , paleontology , biochemistry , chemistry
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 This article is categorized under: Algorithmic Development > Structure Discovery Fundamental Concepts of Data and Knowledge > Knowledge Representation