research-article

Multimodal Meta-Learning for Cold-Start Sequential Recommendation

Authors:

Wayne Xin ZhaoAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 3421 - 3430

https://doi.org/10.1145/3511808.3557101

Published: 17 October 2022 Publication History

Abstract

In this paper, we study the task of cold-start sequential recommendation, where new users with very short interaction sequences come with time. We cast this problem as a few-shot learning problem and adopt a meta-learning approach to developing our solution. For our task, a major obstacle of effective knowledge transfer that is there exists significant characteristic divergence between old and new interaction sequences for meta-learning. To address the above issues, we purpose a Multimodal MetaLearning (denoted as MML) approach that incorporates multimodal side information of items (e.g., text and image) into the meta-learning process, to stabilize and improve the meta-learning process for cold-start sequential recommendation. In specific, we design a group of multimodal meta-learners corresponding to each kind of modality, where ID features are used to develop the main meta-learner and the rest text and image features are used to develop auxiliary meta-learners. Instead of simply combing the predictions from different meta-learners, we design an adaptive, learnable fusion layer to integrate the predictions based on different modalities. Meanwhile, we design a cold-start item embedding generator, which utilize multimodal side information to warm up the ID embeddings of new items. Extensive offline and online experiments demonstrate that MML can significantly improve the recommendation performance for cold-start users compared with baseline models. Our code is released at https://github.com/RUCAIBox/MML.

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Cited By

Liu QHu JXiao YZhao XGao JWang WLi QTang J(2024)Multimodal Recommender Systems: A SurveyACM Computing Surveys10.1145/369546157:2(1-17)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3695461
Zhang LZhou XZeng ZShen Z(2024)Multimodal Pre-training for Sequential Recommendation via Contrastive LearningACM Transactions on Recommender Systems10.1145/36820753:1(1-23)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3682075
Wang YGe YLi ZLi LChen R(2024)M3Rec: A Context-Aware Offline Meta-Level Model-Based Reinforcement Learning Approach for Cold-Start RecommendationACM Transactions on Information Systems10.1145/365994742:6(1-27)Online publication date: 19-Aug-2024
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Index Terms

Multimodal Meta-Learning for Cold-Start Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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https://dl.acm.org/doi/10.1145/3695461
Zhang LZhou XZeng ZShen Z(2024)Multimodal Pre-training for Sequential Recommendation via Contrastive LearningACM Transactions on Recommender Systems10.1145/36820753:1(1-23)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3682075
Wang YGe YLi ZLi LChen R(2024)M3Rec: A Context-Aware Offline Meta-Level Model-Based Reinforcement Learning Approach for Cold-Start RecommendationACM Transactions on Information Systems10.1145/365994742:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3659947
Gong JDing JMeng FChen GChen HZhao SLu HLi YBaeza-Yates RBonchi F(2024)A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671984(896-907)Online publication date: 25-Aug-2024
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Xu ELi XZhou ZJi JZhao JMiao DWang SLiu LXu SSerra ESpezzano F(2024)Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680063(5007-5014)Online publication date: 21-Oct-2024
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Xu XDong HQi LZhang XXiang HXia XXu YDou WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657839(1589-1598)Online publication date: 10-Jul-2024
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Ohtomo KHarakawa RIisaka MIwahashi M(2024)AM-Bi-LSTM: Adaptive Multi-Modal Bi-LSTM for Sequential RecommendationIEEE Access10.1109/ACCESS.2024.335554812(12720-12733)Online publication date: 2024
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Su JChen YLin XZhong JDong C(2024)Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112604305(112604)Online publication date: Dec-2024
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Wei SYang ZZhang JZeng YLi QXiao Y(2024)Click-through conversion rate prediction model of book e-commerce platform based on feature combination and representationExpert Systems with Applications10.1016/j.eswa.2023.122276238(122276)Online publication date: Mar-2024
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Bian SPan XZhao WWang JWang CWen JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Multi-modal Mixture of Experts Represetation Learning for Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614978(110-119)Online publication date: 21-Oct-2023
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