Full Text:   <253>

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CLC number: TP391

On-line Access: 2023-01-21

Received: 2022-04-07

Revision Accepted: 2023-01-21

Crosschecked: 2022-08-02

Cited: 0

Clicked: 194

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yitong WANG

https://orcid.org/0000-0003-4020-5767

Fei CAI

https://orcid.org/0000-0002-5709-1682

Chengyu SONG

https://orcid.org/0000-0001-7020-7603

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.1 P.73-87

http://doi.org/10.1631/FITEE.2200137


Self-supervised graph learning with target-adaptive masking for session-based recommendation


Author(s):  Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG

Affiliation(s):  Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   wangyitong20@nudt.edu.cn, caifei08@nudt.edu.cn, panzhiqiang@nudt.edu.cn, songchengyu@nudt.edu.cn

Key Words:  Session-based recommendation, Self-supervised learning, Graph neural networks, Target-adaptive masking


Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG. Self-supervised graph learning with target-adaptive masking for session-based recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 73-87.

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Abstract: 
session-based recommendation aims to predict the next item based on a user's limited interactions within a short period. Existing approaches use mainly recurrent neural networks (RNNs) or graph neural networks (GNNs) to model the sequential patterns or the transition relationships between items. However, such models either ignore the over-smoothing issue of GNNs, or directly use cross-entropy loss with a softmax layer for model optimization, which easily results in the over-fitting problem. To tackle the above issues, we propose a self-supervised graph learning with target-adaptive masking (SGL-TM) method. Specifically, we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items, which helps supervise the model in generating accurate representations of items in the ongoing session. After that, we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking module. Finally, we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters. Extensive experimental results from two benchmark datasets, Gowalla and Diginetica, indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20, especially in short sessions.

融合自监督图学习与目标自适应屏蔽的会话型推荐方法

王祎童,蔡飞,潘志强,宋城宇
国防科技大学信息系统工程重点实验室,中国长沙市,410073
摘要:会话型推荐旨在根据用户在短时间内有限的交互来预测下一个时间戳将要进行交互的物品。现有模型主要使用循环神经网络(RNN)或图神经网络(GNN)来建模顺序序列或物品之间的传递关系。然而,此类模型要么忽略了GNN的过度平滑问题,要么直接利用交叉熵损失和softmax层进行模型优化,容易导致过拟合问题。为了解决上述问题,本文提出一种融合自监督图学习与目标自适应屏蔽的会话型推荐方法(SGL-TM)。具体来说,首先根据所有涉及到的会话构建全局图,然后从物品之间的全局连接中捕捉自监督信号,用来监督模型生成当前会话中准确的物品表示。之后,通过比较真值与经过我们设计的目标自适应屏蔽模块调整后的物品的预测分数来计算主监督损失。最后,将主监督组件与辅助自监督模块相结合,以获得用来优化模型参数的最终损失。在两个真实数据集(Gowalla和Diginetica)上的大量实验结果表明,SGL-TM在Recall@20和MRR@20方面的性能优于最先进的基准模型,尤其体现在短会话上。

关键词:会话型推荐;自监督学习;图神经网络;目标自适应屏蔽

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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