Continual collaborative filtering through gradient alignment

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets)...

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Bibliographic Details
Main Authors: DO, Dinh Hieu, LAUW, Hady Wirawan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8269
https://ink.library.smu.edu.sg/context/sis_research/article/9272/viewcontent/ContinualCollabFilter_GA_recsys23_av.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users and items over time into a notion of tasks. Of particular concern is to mitigate catastrophic forgetting that occurs when the model would reduce performance for older users and items in prior tasks even as it tries to fit the newer users and items in the current task. To alleviate this, we propose a method that leverages gradient alignment to deliver a model that is more compatible across tasks and maximizes user agreement for better user representations to improve long-term recommendations.