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

doi:10.1631/FITEE.2200137

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融合自监督图学习与目标自适应屏蔽的会话型推荐方法

常规文章 | Updated：2023-01-28

- 融合自监督图学习与目标自适应屏蔽的会话型推荐方法
- Self-supervised graph learning with target-adaptive masking for session-based recommendation
- 信息与电子工程前沿（英文） 2023年24卷第1期页码：73-87
- Affiliations：
  
  Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  E-mail: wangyitong20@nudt.edu.cn;
  ‡Corresponding authors
  panzhiqiang@nudt.edu.cn;
  songchengyu@nudt.edu.cn
- Funds：
- DOI：10.1631/FITEE.2200137
  中图分类号： TP391
- 收稿：2022-04-07，
  
  录用：2022-08-02，
  
  纸质出版：2023-01-0
- Accepted：
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王祎童, 蔡飞, 潘志强, 等. 融合自监督图学习与目标自适应屏蔽的会话型推荐方法[J]. 信息与电子工程前沿（英文）, 2023,24(1):73-87.

Yitong WANG, Fei CAI, Zhiqiang PAN, et al. Self-supervised graph learning with target-adaptive masking for session-based recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 73-87.
王祎童, 蔡飞, 潘志强, 等. 融合自监督图学习与目标自适应屏蔽的会话型推荐方法[J]. 信息与电子工程前沿（英文）, 2023,24(1):73-87. DOI： 10.1631/FITEE.2200137.

Yitong WANG, Fei CAI, Zhiqiang PAN, et al. Self-supervised graph learning with target-adaptive masking for session-based recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 73-87. DOI： 10.1631/FITEE.2200137.

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。

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.

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