Web: http://arxiv.org/abs/2201.11016

Jan. 27, 2022, 2:11 a.m. | Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv recommender systems systems

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