research-article

Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat

Authors:

Chonggang Song,

Chuan ChenAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 4988 - 4994

https://doi.org/10.1145/3583780.3614676

Published: 21 October 2023 Publication History

Abstract

Recently, cross-domain recommendation (CDR) has been widely studied in both research and industry since it can alleviate a long-standing challenge of traditional recommendation methods, i.e., data sparsity issue, by transferring the information from a relatively richer domain (termed source domain) to a sparser domain (termed target domain). To our best knowledge, most (if not all) existing CDR methods focus on transferring either the similar content information or the user preferences embedding from the source domain to the target domain. However, they fail to improve the recommendation performance in real-world recommendation scenarios where the items in the source domain are totally different from those in the target domain in terms of attributes. To solve the above issues, we analyzed the historical interactions of users from different domains in the WeChat platform, and found that if two users have similar interests (interactions) in one domain, they are very likely to have similar interests in another domain even though the items of these two domains are totally different in terms of attributes. Based on this observation, in this paper, we propose a novel model named Dual Interests-Aligned Graph Auto-Encoders (DIAGAE) by utilizing the inter-domain interest alignment of users. Besides, our proposed model DIAGAE also leverages graph decoding objectives to align intra-domain user interests, which makes the representation of two users who have similar interests in a single domain closer. Comprehensive experimental results demonstrate that our model DIAGAE outperforms state-of-the-art methods on both public benchmark datasets and online A/B tests in WeChat live-stream recommendation scenario. Our model DIAGAE now serves the major online traffic in WeChat live-streaming recommendation scenario.

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Cited By

Zhang YWang RLi XYao TEvdokimov AValverde JGao YZhang JEttinger EChi ECheng D(2024)Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688041(829-831)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688041
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Huang XLin DHuang WSun SWen JChen C(2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00191

Index Terms

Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
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University of Wolverhampton, UK
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Program Chairs:
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Spotify, UK
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Tsinghua University, China
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Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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Cited By

Zhang YWang RLi XYao TEvdokimov AValverde JGao YZhang JEttinger EChi ECheng D(2024)Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688041(829-831)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688041
Zheng XLiu WChen CSu JLiao XHu MTan Y(2024)Mining User Consistent and Robust Preference for Unified Cross Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344658136:12(8758-8772)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3446581
Huang XLin DHuang WSun SWen JChen C(2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00191

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