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WIREs Data Mining Knowl Discov
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Tensor methods and recommender systems

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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
Examples of contextual information.
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Higher‐order folding‐in for Tucker decomposition. A slice with new user information in the original data (a) and a corresponding row update of the factor matrix in TD (b) are marked with solid color.
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Tensor of order 3 (a) and its unfolding (b). Arrow denotes the mode of matricization.
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Algorithmic Development > Structure Discovery
Fundamental Concepts of Data and Knowledge > Knowledge Representation

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