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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (6): 46-54    DOI: 10.11925/infotech.2096-3467.2021.1105
Current Issue | Archive | Adv Search |
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.
Key words： Sequential Recommendation      Session      GNN      Bi-GRU      Attention Mechanism     
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
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Ruoqi Zhang
Jianfang Shen
Pinghua Chen

Cite this article:

Zhang Ruoqi, Shen Jianfang, Chen Pinghua. Session Sequence Recommendation with GNN, Bi-GRU and Attention Mechanism. Data Analysis and Knowledge Discovery, 2022, 6(6): 46-54.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1105     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I6/46

Model Architecture
Directed Session Graph
Adjacency Matrix
Bidirectional Gated Recurrent Unit
Gated Recurrent Unit
数据 Yoochoose Diginetica
点击数 557 248 982 961
项目数 16 766 43 097
平均长度 6.16 5.12
Statistics of Datasets
方法

评价指标		 Yoochoose Diginetica
P @ 20 M R R @ 20 P @ 20 M R R @ 20
Item-KNN 51.60 21.81 28.35 9.45
FPMC 45.62 15.01 31.55 8.92
GRU4Rec 60.64 22.89 43.82 15.46
STAMP 68.74 29.67 45.64 14.32
SRGNN 69.27 29.67 49.04 16.53
GNN-DBLGRU-TA 70.23 30.31 50.13 16.63
GNN-BiGRU-TA 70.54 30.69 51.15 17.34
Model Comparison on Yoochoose and Diginetica
属性

数据集		 Diginetica 1 Diginetica 3 Diginatica 5 Diginatica 7
训练集
会话数		 612 858 573 133 455 168 333 119
测试集
会话数		 51 751 48 432 48 432 27 978
项目数 39 056 37 096 31 987 26 336
序列平均
长度		 4.995 3 5.890 2 7.322 3 8.647 4
Different Length Sequence Attributes of Diginetica
The Effect of Sequence Length on Model Performance
Model Comparison
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