Neural Pairwise Ranking Factorization Machine for Item Recommendation. / Jiao, Lihong; Yu, Yonghong; Zhou, Ningning; Zhang, Li; Yin, Hongzhi.

Neural Pairwise Ranking Factorization Machine for Item Recommendation. Proceedings, Part I. ed. Springer, [Cham], 2020. p. 680-688 (Lecture Notes in Computer Science (12112)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

E-pub ahead of print
  • Lihong Jiao
  • Yonghong Yu
  • Ningning Zhou
  • Li Zhang
  • Hongzhi Yin

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 publicationNeural Pairwise Ranking Factorization Machine for Item Recommendation
PublisherSpringer, [Cham]
Pages680-688
Number of pages9
EditionProceedings, Part I.
ISBN (Electronic)9783030594107
ISBN (Print)9783030594091
DOIs
Publication statusE-pub ahead of print - 18 Sep 2020
EventDatabase Systems for Advanced Applications: 25th International Conference, DASFAA 2020 - Jeju, South Korea
Duration: 24 Sep 202027 Sep 2020

Publication series

NameLecture Notes in Computer Science (12112)

Conference

ConferenceDatabase Systems for Advanced Applications: 25th International Conference, DASFAA 2020
CityJeju, South Korea
Period24/09/2027/09/20
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43379928