Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (6): 46-54    DOI: 10.11925/infotech.2096-3467.2021.1105
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Session Sequence Recommendation with GNN, Bi-GRU and Attention Mechanism
Zhang Ruoqi,Shen Jianfang(),Chen Pinghua
School of Computers, Guangdong University of Technology, Guangzhou 510006, China
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Abstract

[Objective] This study aims to improve the traditional session sequence recommendation algorithm, whose one-time modeling technique cannot represent the product’s comprehensive information or capture the user’s global/short-term interests. [Methods] First, we constructed a directed session graph based on the historical sessions, and used the GNN to learn their node information representation to enrich node embeddings. Then, we captured the user’s global and short-term interests in session sequences with Bi-GRU and attention mechanism to generate recommendation lists. [Results] We examined our new algorithm with the Yoochoose and the Diginetica datasets. Compared with the suboptimal model, the Mean Reciprocal Rank of our algorithm improved by 1.02%, and the precision improved by 2.11%, respectively. [Limitations] The proposed model did not work well with the long sequences. [Conclusions] Our new algorithm can more effectively model the user behavior sequence, predict the user’s possible actions, and improve the recommendation lists.

Received: 27 September 2021      Published: 28 July 2022
 ZTFLH: TP391
Fund:Guangdong Key Area R&D Program(2020B0101100001);Guangdong Key Area R&D Program(2021B0101200002);Science and Technology Planning Project of Guangdong Province(2020B1010010010)
Corresponding Authors: Shen Jianfang     E-mail: tysjf@gdut.edu.cn