Time-based Sequence Model for Personalization and Recommendation Systems

• URL: http://arxiv.org/abs/2008.11922v1
• Date: Thu, 27 Aug 2020 05:46:47 GMT
• Title: Time-based Sequence Model for Personalization and Recommendation Systems
• Authors: Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich and Liang Xiong
• Abstract summary: 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. We show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset.
• Score: 6.484371238475296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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