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Personalized Transfer of User Preferences for Cross-domain Recommendation

Published: 15 February 2022 Publication History

Abstract

Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's preferences from the source domain to the target domain, is the key issue in Cross-domain Recommendation (CDR) which is a promising solution to deal with the cold-start problem. Most existing methods model a common preference bridge to transfer preferences for all users. Intuitively, since preferences vary from user to user, the preference bridges of different users should be different. Along this line, we propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR). Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user. To learn the meta network stably, we employ a task-oriented optimization procedure. With the meta-generated personalized bridge function, the user's preference embedding in the source domain can be transformed into the target domain, and the transformed user preference embedding can be utilized as the initial embedding for the cold-start user in the target domain. Using large real-world datasets, we conduct extensive experiments to evaluate the effectiveness of PTUPCDR on both cold-start and warm-start stages. The code has been available at https://github.com/easezyc/WSDM2022-PTUPCDR.

Supplementary Material

MP4 File (WSDM22-fp165.mp4)
We propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR). Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user. To learn the meta network stably, we employ a task-oriented optimization procedure. With the meta-generated personalized bridge function, the user's preference embedding in the source domain can be transformed into the target domain, and the transformed user preference embedding can be utilized as the initial embedding for the cold-start user in the target domain.

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    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: 15 February 2022

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    Author Tags

    1. cold-start problem
    2. cross-domain recommendation
    3. meta network
    4. personalized transfer

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    • the National Natural Science Foundation of China

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    • (2025)metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge LearningJournal of Chemical Information and Modeling10.1021/acs.jcim.4c02193Online publication date: 12-Feb-2025
    • (2025)MIMNet: Multi-interest Meta Network with Multi-granularity Target-guided Attention for cross-domain recommendationNeurocomputing10.1016/j.neucom.2024.129208620(129208)Online publication date: Mar-2025
    • (2025)Cross-domain recommendation via knowledge distillationKnowledge-Based Systems10.1016/j.knosys.2025.113112311(113112)Online publication date: Feb-2025
    • (2025)TJMN: Target-enhanced joint meta network with contrastive learning for cross-domain recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112919310(112919)Online publication date: Feb-2025
    • (2025)Deep User Rating Pattern Mining and Fusion Inference Method for Cross-Domain RecommendationExpert Systems with Applications10.1016/j.eswa.2024.126374269(126374)Online publication date: Apr-2025
    • (2025)Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorizationExpert Systems with Applications10.1016/j.eswa.2024.125667263(125667)Online publication date: Mar-2025
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    • (2025)Disentangled Representations for Cross-Domain Recommendation via Heterogeneous Graph Contrastive LearningDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_3(35-50)Online publication date: 12-Jan-2025
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