Neural Pairwise Ranking Factorization Machine for Item Recommendation

Lihong Jiao, Yonghong Yu, Ningning Zhou, Li Zhang, Hongzhi Yin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, 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, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models.
Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publication25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part I
EditorsYunmook Nah, Bin Cui, Jeffrey Xu Yu, Yang-Sae Moon, Sang-Won Lee, Steven Euijong Whang
Place of PublicationCham
PublisherSpringer
Pages680-688
Number of pages9
ISBN (Electronic)9783030594107
ISBN (Print)9783030594091
DOIs
Publication statusPublished - 2020
Event25th International Conference on Database Systems for Advanced Applications - Jeju, Korea, Republic of
Duration: 24 Sep 202027 Sep 2020
http://db.pknu.ac.kr/dasfaa2020/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2020
CountryKorea, Republic of
CityJeju
Period24/09/2027/09/20
Internet address

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