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User-Video Co-Attention Network for Personalized Micro-video Recommendation

Authors:

Zhenzhong Chen,

Xinghai HuAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 3020 - 3026

https://doi.org/10.1145/3308558.3313513

Published: 13 May 2019 Publication History

Abstract

With the increasing popularity of micro-video sharing where people shoot short-videos effortlessly and share their daily stories on social media platforms, the micro-video recommendation has attracted extensive research efforts to provide users with micro-videos that interest them. In this paper, a hypothesis we explore is that, not only do users have multi-modal interest, but micro-videos have multi-modal targeted audience segments. As a result, we propose a novel framework User-Video Co-Attention Network (UVCAN), which can learn multi-modal information from both user and microvideo side using attention mechanism. In addition, UVCAN reasons about the attention in a stacked attention network fashion for both user and micro-video. Extensive experiments on two datasets collected from Toffee present superior results of our proposed UVCAN over the state-of-the-art recommendation methods, which demonstrate the effectiveness of the proposed framework.

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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Zhu XLi LLi J(2025)To offer or not to offer? Bullet screen strategies for competing video platforms with vertical differentiationJournal of Retailing and Consumer Services10.1016/j.jretconser.2024.10408382(104083)Online publication date: Jan-2025
https://doi.org/10.1016/j.jretconser.2024.104083
Zhou BLiang Y(2024)UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal RecommendationApplied Sciences10.3390/app14221018714:22(10187)Online publication date: 6-Nov-2024
https://doi.org/10.3390/app142210187
Zhang DZhu ZWang ZWang JXiao LChen YZhao D(2024)Personalized News Recommendation Method with Double-Layer Residual Connections and Double Multi-Head Self-Attention MechanismsApplied Sciences10.3390/app1413566714:13(5667)Online publication date: 28-Jun-2024
https://doi.org/10.3390/app14135667
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
Zhao HCai GZhu JDong ZXu JWen JBaeza-Yates RBonchi F(2024)Counteracting Duration Bias in Video Recommendation via Counterfactual Watch TimeProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671817(4455-4466)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671817
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
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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Saket SJeunen OKalim MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Monitoring the Evolution of Behavioural Embeddings in Social Media RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661368(2935-2939)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661368
Shang YGao CChen JJin DLi YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Improving Item-side Fairness of Multimodal Recommendation via Modality DebiasingProceedings of the ACM Web Conference 202410.1145/3589334.3648156(4697-4705)Online publication date: 13-May-2024
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Fan FSu YNie LJing PHong DLiu Y(2024)Dual-Domain Aligned Deep Hierarchical Matrix Factorization Method for Micro-Video Multi-Label ClassificationIEEE Transactions on Multimedia10.1109/TMM.2023.330122426(2598-2607)Online publication date: 1-Jan-2024
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