MMMLP : Multi-modal Multilayer Perceptron for Sequential Recommendations

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

4 Scopus Citations
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Author(s)

  • Jiahao Liang
  • Muyang Li
  • Haochen Liu
  • Zitao Liu

Detail(s)

Original languageEnglish
Title of host publicationWWW '23: Proceedings of the ACM Web Conference 2023
EditorsYing Ding, Jie Tang
PublisherAssociation for Computing Machinery, Inc
Pages1109-1117
ISBN (Print)9781450394161
Publication statusPublished - 30 Apr 2023

Publication series

NameACM Web Conference - Proceedings of the World Wide Web Conference, WWW

Conference

TitleACM Web Conference 2023 (WWW '23)
LocationHybrid
PlaceUnited States
CityAustin
Period30 April - 4 May 2023

Abstract

Sequential recommendation aims to offer potentially interesting products to users by capturing their historical sequence of interacted items. Although it has facilitated extensive physical scenarios, sequential recommendation for multi-modal sequences has long been neglected. Multi-modal data that depicts a user's historical interactions exists ubiquitously, such as product pictures, textual descriptions, and interacted item sequences, providing semantic information from multiple perspectives that comprehensively describe a user's preferences. However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. MMMLP is a purely MLP-based architecture that consists of three modules - the Feature Mixer Layer, Fusion Mixer Layer, and Prediction Layer - and has an edge on both efficacy and efficiency. Extensive experiments show that MMMLP achieves state-of-the-art performance with linear complexity. We also conduct ablating analysis to verify the contribution of each component. Furthermore, compatible experiments are devised, and the results show that the multi-modal representation learned by our proposed model generally benefits other recommendation models, emphasizing our model's ability to handle multi-modal information. We have made our code available online to ease reproducibility1. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Research Area(s)

  • Multi-modal Data, Multimedia, Sequential Recommendation

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations. / Liang, Jiahao; Zhao, Xiangyu; Li, Muyang et al.
WWW '23: Proceedings of the ACM Web Conference 2023. ed. / Ying Ding; Jie Tang. Association for Computing Machinery, Inc, 2023. p. 1109-1117 (ACM Web Conference - Proceedings of the World Wide Web Conference, WWW).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review