BA-GNN: Behavior-aware graph neural network for session-based recommendation

doi:10.1007/s11704-022-2324-x

Frontiers of Computer Science

2023, Vol. 17

Issue (6): 176613 https://doi.org/10.1007/s11704-022-2324-x

本期目录

BA-GNN: Behavior-aware graph neural network for session-based recommendation

Yongquan LIANG¹, Qiuyu SONG¹, Zhongying ZHAO¹(

), Hui ZHOU², Maoguo GONG^1,²(

)

¹. School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
². School of Electronic Engineering, Xidian University, Xi’an 710071, China

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Abstract：

Session-based recommendation is a popular research topic that aims to predict users’ next possible interactive item by exploiting anonymous sessions. The existing studies mainly focus on making predictions by considering users’ single interactive behavior. Some recent efforts have been made to exploit multiple interactive behaviors, but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences. To address these problems, we propose a behavior-aware graph neural network for session-based recommendation. First, different interactive sequences are modeled as directed graphs. Thus, the item representations are learned via graph neural networks. Then, a sparse self-attention module is designed to remove the noise in behavior sequences. Finally, the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations. Experimental results on two public datasets show that our proposed method outperforms all competitive baselines. The source code is available at the website of GitHub.

Key words： session-based recommendation multiple interactive behaviors graph neural networks

收稿日期: 2022-05-29 出版日期: 2023-02-13

Corresponding Author(s): Zhongying ZHAO,Maoguo GONG

引用本文:

. [J]. Frontiers of Computer Science, 2023, 17(6): 176613.
Yongquan LIANG, Qiuyu SONG, Zhongying ZHAO, Hui ZHOU, Maoguo GONG. BA-GNN: Behavior-aware graph neural network for session-based recommendation. Front. Comput. Sci., 2023, 17(6): 176613.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-022-2324-x
https://academic.hep.com.cn/fcs/CN/Y2023/V17/I6/176613

Fig.1

Notations	Descriptions
$V$	The item set.
$m$	The number of items.
$s$	The sequence of items for session $s$ .
$s (c)$	The click sequence of session $s$ .
$\| s (c) \|$	The number of items in the click sequence $s (c)$ .
$s (p)$	The purchase sequence of session $s$ .
$\| s (p) \|$	The number of items in the purchase sequence $s (p)$ .
$G c$	The graph of the click sequence $s (c)$ .
$G p$	The graph of the purchase sequence $s (p)$ .
$A c$	The adjacency matrix of graph $G c$ .
$A p$	The adjacency matrix of graph $G p$ .
$v c, i$	The representation of node $v c, i$ .
$v p, i$	The representation of node $v p, i$ .
$v g, i$	The global representation of node $v i$ .
$z s (c)$	The representation of the click sequence $s (c)$ .
$z s (p)$	The representation of the purchase sequence $s (p)$ .
$α$	The weight of the click sequence $s (c)$ .
$s$	The representation of the session $s$ .

Tab.1

Fig.2

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Tab.5

Fig.3

Fig.4

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