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.
@article{arxiv.2008.11922,
title = {Time-based Sequence Model for Personalization and Recommendation Systems},
author = {Tigran Ishkhanov and Maxim Naumov and Xianjie Chen and Yan Zhu and Yuan Zhong and Alisson Gusatti Azzolini and Chonglin Sun and Frank Jiang and Andrey Malevich and Liang Xiong},
journal= {arXiv preprint arXiv:2008.11922},
year = {2020}
}