Generalized User Representations for Transfer Learning

• URL: http://arxiv.org/abs/2403.00584v1
• Date: Fri, 1 Mar 2024 15:05:21 GMT
• Title: Generalized User Representations for Transfer Learning
• Authors: Ghazal Fazelnia, Sanket Gupta, Claire Keum, Mark Koh, Ian Anderson, and Mounia Lalmas
• Abstract summary: We present a novel framework for user representation in large-scale recommender systems. Our approach employs a two-stage methodology combining representation learning and transfer learning. We show how the proposed framework can significantly reduce infrastructure costs compared to alternative approaches.
• Score: 6.953653891411339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach employs a two-stage methodology combining representation learning and transfer learning. The representation learning model uses an autoencoder that compresses various user features into a representation space. In the second stage, downstream task-specific models leverage user representations via transfer learning instead of curating user features individually. We further augment this methodology on the representation's input features to increase flexibility and enable reaction to user events, including new user experiences, in Near-Real Time. Additionally, we propose a novel solution to manage deployment of this framework in production models, allowing downstream models to work independently. We validate the performance of our framework through rigorous offline and online experiments within a large-scale system, showcasing its remarkable efficacy across multiple evaluation tasks. Finally, we show how the proposed framework can significantly reduce infrastructure costs compared to alternative approaches.

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