Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2023

Abstract

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.

Keywords

collaborative filtering, continual learning, gradient alignment, recommendation systems

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems, Singapore, September 18-22

First Page

1133

Last Page

1138

ISBN

9798400702419

Identifier

10.1145/3604915.3610648

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3604915.3610648

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