Tensor Methods and Recommender Systems

TL;DR

This paper surveys the recent advances in tensor factorization techniques and their application in recommender systems, highlighting their ability to incorporate complex contextual information for improved recommendations.

Contribution

It provides the first comprehensive overview of tensor-based recommender models across various domains, consolidating studies and discussing future research directions.

Findings

Tensor methods enhance recommendation accuracy by modeling multifaceted data.

Tensor-based models are effective in context-aware and criteria-driven recommendations.

The survey identifies key challenges and future opportunities in tensor recommender systems.

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, 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 in an easily readable, digestible format, 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.