Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Tensor methods and recommender systems

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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

Examples of contextual information.
[ Normal View | Magnified View ]
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.
[ Normal View | Magnified View ]
Tensor of order 3 (a) and its unfolding (b). Arrow denotes the mode of matricization.
[ Normal View | Magnified View ]

Related Articles

An overview on the exploitation of time in collaborative filtering
Applications of tensor (multiway array) factorizations and decompositions in data mining
Multidimensional compressed sensing and their applications

Browse by Topic

Algorithmic Development > Structure Discovery
Fundamental Concepts of Data and Knowledge > Knowledge Representation

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts