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Heterogeneous Side Information-based Iterative Guidance Model for Recommendation

Authors:

Weiping WangAuthors Info & Claims

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

Pages 55 - 63

https://doi.org/10.1145/3460426.3463631

Published: 01 September 2021 Publication History

Abstract

Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.

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Cited By

Ayemowa MIbrahim RAdamu Bena Y(2024)A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsIEEE Access10.1109/ACCESS.2024.346275012(139524-139539)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462750
Hong MChung NKoo CKoh S(2023)TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction dataInternational Journal of Data Science and Analytics10.1007/s41060-022-00380-716:2(147-162)Online publication date: 19-Jan-2023
https://doi.org/10.1007/s41060-022-00380-7
Qian MDai FGu XFan HLiu DLi B(2023)User Popularity Preference Aware Sequential RecommendationComputational Science – ICCS 202310.1007/978-3-031-35995-8_8(104-118)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35995-8_8
Show More Cited By

Index Terms

Heterogeneous Side Information-based Iterative Guidance Model for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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Published In

cover image ACM Conferences

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

August 2021

715 pages

ISBN:9781450384636

DOI:10.1145/3460426

General Chairs:
Wen-Huang Cheng
National Yang Ming Chiao Tung University, Taiwan
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Meng Wang
Hefei University of Technology, China
,
Program Chairs:
Wei-Ta Chu
National Cheng Kung University, Taiwan
,
Jiaying Liu
Peking University, China
,
Marcel Worring
University of Amsterdam, Netherlands

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Published: 01 September 2021

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ICMR '21: International Conference on Multimedia Retrieval

August 21 - 24, 2021

Taipei, Taiwan

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Cited By

Ayemowa MIbrahim RAdamu Bena Y(2024)A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsIEEE Access10.1109/ACCESS.2024.346275012(139524-139539)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462750
Hong MChung NKoo CKoh S(2023)TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction dataInternational Journal of Data Science and Analytics10.1007/s41060-022-00380-716:2(147-162)Online publication date: 19-Jan-2023
https://doi.org/10.1007/s41060-022-00380-7
Qian MDai FGu XFan HLiu DLi B(2023)User Popularity Preference Aware Sequential RecommendationComputational Science – ICCS 202310.1007/978-3-031-35995-8_8(104-118)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35995-8_8
Dai YMeng SLiu QLiu X(2022)Knowledge-aware Graph Attention Network with Distributed & Cross Learning for Collaborative Recommendation2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00044(294-301)Online publication date: Dec-2022
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00044

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