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: 1165
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0003-4020-5767
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,in press.https://doi.org/10.1631/FITEE.2200137 @article{title="Self-supervised graph learning with target-adaptive masking for session-based recommendation", %0 Journal Article TY - JOUR
融合自监督图学习与目标自适应屏蔽的会话型推荐方法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。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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