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Time-based Sequence Model for Personalization and Recommendation Systems
Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhon · 2020-08-27 · via cs.LG updates on arXiv.org

In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with inner products in different vector spaces, that can be thought of as a modification of multi-headed attention. This mechanism allows the model to efficiently treat sequences of user behavior of different length. We study the properties of our state-of-the-art model on statistically designed data set. Also, we show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset.

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