Recency Dropout for Recurrent Recommender Systems

• URL: http://arxiv.org/abs/2201.11016v1
• Date: Wed, 26 Jan 2022 15:50:20 GMT
• Title: Recency Dropout for Recurrent Recommender Systems
• Authors: Bo Chang, Can Xu, Matthieu L\^e, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen
• Abstract summary: We introduce the recency dropout technique, a simple yet effective data augmentation technique to alleviate the recency bias in recommender systems. We demonstrate the effectiveness of recency dropout in various experimental settings including a simulation study, offline experiments, as well as live experiments on a large-scale industrial recommendation platform.
• Score: 23.210278548403185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories. However, recurrent neural networks (RNNs) are known to have difficulty learning long-term dependencies. As a consequence, RNN-based recommender systems tend to overly focus on short-term user interests. This is referred to as the recency bias, which could negatively affect the long-term user experience as well as the health of the ecosystem. In this paper, we introduce the recency dropout technique, a simple yet effective data augmentation technique to alleviate the recency bias in recurrent recommender systems. We demonstrate the effectiveness of recency dropout in various experimental settings including a simulation study, offline experiments, as well as live experiments on a large-scale industrial recommendation platform.

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