short-paper

Cross-modal Collaborative Manifold Propagation for Image Recommendation

Authors:

Lina HuoAuthors Info & Claims

ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

Pages 344 - 348

https://doi.org/10.1145/3323873.3325054

Published: 05 June 2019 Publication History

Abstract

With the rapid evolution of social networks, the increasing user intention gap and visual semantic gap both bring great challenge for users to access satisfied contents. It becomes promising to investigate users' customized multimedia recommendation. In this paper, we propose cross-modal collaborative manifold propagation (CMP) for image recommendation. CMP leverages users' interest distribution to propagate images' user records, which lets users know the trend from others and produces interest-aware image candidates upon users' interests. Visual distribution is investigated simultaneously to propagate users' visual records along dense semantic visual manifold. Visual manifold propagation helps to estimate semantic accurate user-image correlations for the candidate images in recommendation ranking. Experimental performance demonstrate the collaborative user-image inferring ability of CMP with effective user interest manifold propagation and semantic visual manifold propagation in personalized image recommendation.

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Cited By

Ma HQi ZDong XLi XZheng YMeng XMeng L(2023)Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191979(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191979
Jian MGuo JFu XWu LJia T(2022)Cross-Modal Manifold Propagation for Image RecommendationApplied Sciences10.3390/app1206318012:6(3180)Online publication date: 21-Mar-2022
https://doi.org/10.3390/app12063180
Wu BHe XChen YNie LZheng KYe Y(2022)Modeling Product’s Visual and Functional Characteristics for Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299179334:3(1330-1343)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2991793
Show More Cited By

Index Terms

Cross-modal Collaborative Manifold Propagation for Image Recommendation
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery

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Information

Published In

cover image ACM Conferences

ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

June 2019

427 pages

ISBN:9781450367653

DOI:10.1145/3323873

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Alberto Del Bimbo
University of Florence, Italy
,
Zhongfei Zhang
Binghamton University, State University of New York, USA
,
Program Chairs:
Alexander Hauptmann
Carnegie Mellon University, USA
,
K. Selcuk Candan
Arizona State University, USA
,
Marco Bertini
University of Florence, Italy
,
Lexing Xie
Australia National University, Australia
,
Xiao-Yong Wei
Sichuan University, China

Copyright © 2019 ACM.

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Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2019

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Funding Sources

National Natural Science Foundation of China
Beijing Municipal Education Committee Science Foundation

Conference

ICMR '19

Sponsor:

SIGMM

ICMR '19: International Conference on Multimedia Retrieval

June 10 - 13, 2019

Ottawa ON, Canada

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Ma HQi ZDong XLi XZheng YMeng XMeng L(2023)Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191979(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191979
Jian MGuo JFu XWu LJia T(2022)Cross-Modal Manifold Propagation for Image RecommendationApplied Sciences10.3390/app1206318012:6(3180)Online publication date: 21-Mar-2022
https://doi.org/10.3390/app12063180
Wu BHe XChen YNie LZheng KYe Y(2022)Modeling Product’s Visual and Functional Characteristics for Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299179334:3(1330-1343)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2991793
Paul AWu ZLiu KGong S(2022)Personalized recommendation: From clothing to academicMultimedia Tools and Applications10.1007/s11042-022-12259-781:10(14573-14588)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11042-022-12259-7
Jian MGuo JShi GWu LWang Z(2022)Multimodal collaborative graph for image recommendationApplied Intelligence10.1007/s10489-022-03304-x53:1(560-573)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-022-03304-x
Paul AWu ZLiu KGong S(2021)Robust multi-objective visual bayesian personalized ranking for multimedia recommendationApplied Intelligence10.1007/s10489-021-02355-w52:4(3499-3510)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1007/s10489-021-02355-w

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