Global and Local Tensor Factorization for Multi-criteria Recommender System

Shuliang Wang; Jingting Yang; Zhengyu Chen; Hanning Yuan; Jing Geng; Zhen Hai

doi:10.1016/j.patter.2020.100023

Global and Local Tensor Factorization for Multi-criteria Recommender System

Patterns (N Y). 2020 May 8;1(2):100023. doi: 10.1016/j.patter.2020.100023.

Authors

Shuliang Wang^{1

2}, Jingting Yang³, Zhengyu Chen⁴, Hanning Yuan¹, Jing Geng^{1

2}, Zhen Hai⁵

Affiliations

¹ School of Computer Science, Beijing Institute of Technology, Beijing 100081, China.
² Institute of E-Government, Beijing Institute of Technology, Beijing 100081, China.
³ College of Computer & Network Engineering, Shanxi Datong University, Datong 037009, China.
⁴ College of Computer Science & Technology, Zhejiang University, Hangzhou 310007, China.
⁵ Institute for Infocomm Research, 1 Fusionopolis Way, Singapore 138632, Singapore.

Abstract

In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods.

Keywords: big data; global and local tensor factorization (GLTF); matrix factorization; multi-criteria recommender systems; recommender systems; tensor factorization.