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MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation

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Shu-Tao XiaAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6548 - 6557

https://doi.org/10.1145/3581783.3611967

Published: 27 October 2023 Publication History

Abstract

The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.

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Cited By

Lin JDai XXi YLiu WChen BZhang HLiu YWu CLi XZhu CGuo HYu YTang RZhang W(2025)How Can Recommender Systems Benefit from Large Language Models: A SurveyACM Transactions on Information Systems10.1145/367800443:2(1-47)Online publication date: 18-Jan-2025
https://dl.acm.org/doi/10.1145/3678004
Zhang ZLi ZJin JGao XYang XZhang BXiao L(2025)DeepMIN: Deep Multi-modal Interest Network with Cognitive Learning ModulesDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_14(212-227)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_14
Lin JLi QXie GGuan ZJiang YXu TZhang ZZhao PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680615(7581-7590)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680615
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Index Terms

MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
1. Information systems

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Published In

cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 27 October 2023

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Natural Science Foundation of Guangdong Province
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Major Key Project of PCL for Experiments and Applications
Overseas Cooperation Research Fund of Tsinghua Shenzhen International Graduate School
Shenzhen Science and Technology Program
Basic Research Fund of Shenzhen City
National Natural Science Foundation of China

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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https://dl.acm.org/doi/10.1145/3678004
Zhang ZLi ZJin JGao XYang XZhang BXiao L(2025)DeepMIN: Deep Multi-modal Interest Network with Cognitive Learning ModulesDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_14(212-227)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_14
Lin JLi QXie GGuan ZJiang YXu TZhang ZZhao PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680615(7581-7590)Online publication date: 28-Oct-2024
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Wang JRathi PSundaram H(2024)A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688145(433-443)Online publication date: 8-Oct-2024
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Li SGuo HTang XTang RHou LLi RZhang R(2024)Embedding Compression in Recommender Systems: A SurveyACM Computing Surveys10.1145/363784156:5(1-21)Online publication date: 12-Jan-2024
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Liu QZhu JYang YDai QDu ZWu XZhao ZZhang RDong ZBaeza-Yates RBonchi F(2024)Multimodal Pretraining, Adaptation, and Generation for Recommendation: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671473(6566-6576)Online publication date: 25-Aug-2024
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https://dl.acm.org/doi/10.1145/3626772.3657763
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