Nonindependent Session Recommendation Based on Ordinary Differential Equation

Zhenyu Yang, Mingge Zhang, Guojing Liu and Mingyu Li

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract:

The recommendation method based on user sessions is mainly to model sessions as sequences in the assumption that user behaviors are independent and identically distributed, and then to use deep semantic information mining through Deep Neural Networks. Nevertheless, user behaviors may be a nonindependent intention at irregular points in time. For example, users may buy painkillers, books, or clothes for different reasons at different times. However, this has not been taken seriously in previous studies. Therefore, we propose a session recommendation method based on Neural Differential Equations in an attempt to predict user behavior forward or backward from any point in time. We used Ordinary Differential Equations to train the Graph Neural Network and could predict forward or backward at any point in time to model the user's nonindependent sessions. We tested for four real datasets and found that our model achieved the expected results and was superior to the existing session-based recommendations.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9290576

DOI: 10.1155/2020/9290576

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