Enhanced factorization machine via neural pairwise ranking and attention networks

Yu, Yonghong, Jiao, Lihong, Zhou, Ningning, Zhang, Li and Yin, Hongzhi (2020) Enhanced factorization machine via neural pairwise ranking and attention networks. Pattern Recognition Letters, 140. pp. 348-357. ISSN 0167-8655

[img] Text
2020PRL.pdf - Accepted Version
Restricted to Repository staff only until 11 November 2021.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (853kB) | Request a copy
Official URL: https://doi.org/10.1016/j.patrec.2020.11.010

Abstract

The factorization machine models attract significant attention nowadays since they improve recommendation performance by incorporating context information into recommendation modeling. However, traditional factorization machine models often adopt the point-wise learning method for model parameter learning, as well as only model the linear interactions between features. They substantially fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this research, we propose a neural pairwise ranking factorization machine for item recommendation, namely NPRFM, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, instead of the prediction of the absolute scores, the pair-wise ranking model is adopted to learn the relative preferences of users. Since NPRFM does not take into account the importance of feature interactions, we propose a new variant of NPRFM, which learns the importance of feature interactions by introducing the attention mechanism. The empirical results on real-world datasets indicate that the proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models.

Item Type: Article
Uncontrolled Keywords: Recommendation algorithm, Factorization machine, Neural networks
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 30 Nov 2020 13:48
Last Modified: 30 Nov 2020 14:30
URI: http://nrl.northumbria.ac.uk/id/eprint/44866

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics