Adversarial Training for Review-Based Recommendations

Dimitrios Rafailidis*, Fabio Crestani

*Corresponding author for this work

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

Abstract

Recent studies have shown that incorporating users' reviews into the collaborative filtering strategy can significantly boost the recommendation accuracy. A pressing challenge resides on learning how reviews influence users' rating behaviors. In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR. We design a neural architecture of sequence-to-sequence learning to calculate the deep representations of users' reviews on items following an adversarial training strategy. At the same time we jointly learn to factorize the rating matrix, by regularizing the deep representations of reviews with the user and item latent features. In doing so, our model captures the non-linear associations among reviews and ratings while producing a review for each user-item pair. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed model, outperforming other state-of-the-art methods.

Original languageEnglish
Title of host publicationSIGIR'19: Proceedings of the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1057-1060
Number of pages4
DOIs
Publication statusPublished - 2019
Event42nd International ACM SIGIR Conference on Research and Development in Information Retrieval - Paris, France
Duration: 21 Jul 201925 Jul 2019
Conference number: 42

Conference

Conference42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Abbreviated titleSIGIR 2019
CountryFrance
CityParis
Period21/07/1925/07/19

Keywords

  • Recommendation systems
  • adversarial training
  • neural models

Cite this