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Learning Fine-grained User Interests for Micro-video Recommendation

Authors:

Jiansheng Chen,

Yong LiAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 433 - 442

https://doi.org/10.1145/3539618.3591713

Published: 18 July 2023 Publication History

Abstract

Recent years have witnessed the rapid development of online micro-video platforms, in which the recommender system plays an essential role in overcoming the information overloading problem and providing personalized content for users. Although some progress has been achieved in the micro-video recommendation, there are still some limitations in learning the representations of user interests and video features. Specifically, the user modeling in existing works is performed at a coarse-grained level, i.e., video level. However, in micro-video recommendation, the user feedback is at a continuous form---users can skip over a video at each frame---which reveals fine-grained user preferences. In this work, we approach the problem of learning fine-grained user preferences for micro-video recommendation by first collecting two real-world datasets. To address the challenges of preference modeling and weak supervision signal, we propose a solution named FRAME (short for Fine-gRAined preference-modeling for Micro-video rEcommendation). Specifically, we first adopt visual feature extraction and transformation to maintain the fine-grained video embeddings. We then propose graph convolution layers to learn the user preference from complex and fine-grained user-clip relations, and hybrid-supervision objectives for enhancing the supervision signal. The experimental results on two collected real-world datasets demonstrate the effectiveness of our proposed model. We release the datasets and codes in https://github.com/tsinghua-fib-lab/FRAME, which we believe can benefit the community.

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Cited By

Xv GLi XXie RLin CLiu CXia FKang ZLin LBaeza-Yates RBonchi F(2024)Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User FeedbackProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671703(3645-3656)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671703
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
https://dl.acm.org/doi/10.1145/3637528.3671473
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295:COnline publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111841
Show More Cited By

Index Terms

Learning Fine-grained User Interests for Micro-video Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 Owner/Author.

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

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

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The National Natural Science Foundation of China
The Fellowship of China Postdoctoral Science Foundation
The National Key Research and Development Program of China

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Xv GLi XXie RLin CLiu CXia FKang ZLin LBaeza-Yates RBonchi F(2024)Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User FeedbackProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671703(3645-3656)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671703
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
https://dl.acm.org/doi/10.1145/3637528.3671473
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295:COnline publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111841
Gu PHu HWang DYu DXu G(2024)Temporal Diversity-Aware Micro-Video Recommendation with Long- and Short-Term Interests ModelingNeural Processing Letters10.1007/s11063-024-11652-756:3Online publication date: 3-Jun-2024
https://doi.org/10.1007/s11063-024-11652-7
Fan FSu YLiu YJing PQu K(2024)A deep low-rank semantic factorization method for micro-video multi-label classificationMultimedia Systems10.1007/s00530-024-01428-330:4Online publication date: 5-Aug-2024
https://doi.org/10.1007/s00530-024-01428-3
Patil SPatil RKotwal A(2023)Micro video recommendation in multimodality using dual-perception and gated recurrent graph neural networkMultimedia Tools and Applications10.1007/s11042-023-17093-z83:17(51559-51588)Online publication date: 13-Nov-2023
https://doi.org/10.1007/s11042-023-17093-z

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