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Debiasing Learning based Cross-domain Recommendation

Published: 14 August 2021 Publication History

Abstract

As it becomes prevalent that user information exists in multiple platforms or services, cross-domain recommendation has been an important task in industry. Although it is well known that users tend to show different preferences in different domains, existing studies seldom model how domain biases affect user preferences. Focused on this issue, we develop a casual-based approach to mitigating the domain biases when transferring the user information cross domains. To be specific, this paper presents a novel debiasing learning based cross-domain recommendation framework with causal embedding. In this framework, we design a novel Inverse-Propensity-Score (IPS) estimator designed for cross-domain scenario, and further propose three kinds of restrictions for propensity score learning. Our framework can be generally applied to various recommendation algorithms for cross-domain recommendation. Extensive experiments on both public and industry datasets have demonstrated the effectiveness of the proposed framework.

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  • (2025)Invariant debiasing learning for recommendation via biased imputationInformation Processing & Management10.1016/j.ipm.2024.10402862:3(104028)Online publication date: May-2025
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    cover image ACM Conferences
    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
    August 2021
    4259 pages
    ISBN:9781450383325
    DOI:10.1145/3447548
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    Published: 14 August 2021

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    Author Tags

    1. causal embedding
    2. cross-domain recommendation
    3. debiasing learning

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    • (2025)Invariant debiasing learning for recommendation via biased imputationInformation Processing & Management10.1016/j.ipm.2024.10402862:3(104028)Online publication date: May-2025
    • (2024)Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693387(32455-32470)Online publication date: 21-Jul-2024
    • (2024)Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645357(3195-3206)Online publication date: 13-May-2024
    • (2024)Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsProceedings of the ACM Web Conference 202410.1145/3589334.3645351(3173-3184)Online publication date: 13-May-2024
    • (2024)Domain-Oriented Knowledge Transfer for Cross-Domain RecommendationIEEE Transactions on Multimedia10.1109/TMM.2024.339468626(9539-9550)Online publication date: 29-Apr-2024
    • (2024)Contrastive cross-domain sequential recommendation via emphasized intention featuresNeural Networks10.1016/j.neunet.2024.106488179(106488)Online publication date: Nov-2024
    • (2023)A Semantic-Enhancement-Based Social Network User-Alignment AlgorithmEntropy10.3390/e2501017225:1(172)Online publication date: 15-Jan-2023
    • (2023)DA-DAN: A Dual Adversarial Domain Adaption Network for Unsupervised Non-overlapping Cross-domain RecommendationACM Transactions on Information Systems10.1145/361782542:2(1-27)Online publication date: 26-Aug-2023
    • (2023)Sequential Recommendation via an Adaptive Cross-domain Knowledge DecompositionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615058(3453-3463)Online publication date: 21-Oct-2023
    • (2023)Connecting Unseen Domains: Cross-Domain Invariant Learning in RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591965(1894-1898)Online publication date: 19-Jul-2023
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