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Causal Disentangled Recommendation against User Preference Shifts

Published: 18 August 2023 Publication History

Abstract

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on learning robust representations or predicting the shifting pattern. There lacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand the preference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences. Assuming user preference is stable within a short period, we abstract the interaction sequence as a set of chronological environments. From the causal graph, we find that the changes of some unobserved factors (e.g., becoming pregnant) cause preference shifts between environments. Besides, the fine-grained user preference over item categories sparsely affects the interactions with different items. Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: (1) capturing the preference shifts across environments for accurate preference prediction and (2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference. To this end, we propose a Causal Disentangled Recommendation (CDR) framework, which captures preference shifts via a temporal variational autoencoder and learns the sparse influence from multiple environments. Specifically, an encoder is adopted to infer the unobserved factors from user interactions while a decoder is to model the interaction generation process. Besides, we introduce two learnable matrices to disentangle the sparse influence from user preference to interactions. Last, we devise a multi-objective loss to optimize CDR. Extensive experiments on three datasets show the superiority of CDR in enhancing the generalization ability under user preference shifts.

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  • (2024)Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferencesNeural Computing and Applications10.1007/s00521-024-09447-x36:13(7085-7103)Online publication date: 18-Feb-2024

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    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 42, Issue 1
    January 2024
    924 pages
    EISSN:1558-2868
    DOI:10.1145/3613513
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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 August 2023
    Online AM: 18 April 2023
    Accepted: 27 March 2023
    Revised: 08 January 2023
    Received: 03 July 2022
    Published in TOIS Volume 42, Issue 1

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    Author Tags

    1. Causal disentangled recommendation
    2. preference shifts
    3. generalizable recommendation
    4. out-of-distribution generalization

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    • Defence Science and Technology Agency
    • National Natural Science Foundation of China

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    • (2024)Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferencesNeural Computing and Applications10.1007/s00521-024-09447-x36:13(7085-7103)Online publication date: 18-Feb-2024

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