The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism

QIAN Zhong-sheng; HUANG Heng; WAN Zi-long

doi:10.12263/DZXB.20231084

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The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism

PAPERS | 更新时间：2025-12-11

- The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism
- ACTA ELECTRONICA SINICA Vol. 52, Issue 11, Pages: 3684-3698(2024)
- 作者机构：
  
  江西财经大学计算机与人工智能学院，江西南昌 330013
- 作者简介：
- 基金信息：
  
  Foundation Item(s):　Foundation Item(s): National Natural Science Foundation of China(62262025);Jiangxi Provincial Natural Science Foundation(20224ACB202012)
- DOI：10.12263/DZXB.20231084
  CLC： TP399
- Received：20 November 2023，
  
  Revised：2024-04-22，
  
  Published：25 November 2024
- 稿件说明：
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钱忠胜, 黄恒, 万子珑. 融合自注意力机制的多行为图对比学习推荐方法[J]. 电子学报, 2024, 52(11): 3684-3698.

QIAN Zhong-sheng, HUANG Heng, WAN Zi-long. The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism[J]. Acta Electronica Sinica, 2024, 52(11): 3684-3698.
钱忠胜, 黄恒, 万子珑. 融合自注意力机制的多行为图对比学习推荐方法[J]. 电子学报, 2024, 52(11): 3684-3698. DOI：10.12263/DZXB.20231084

QIAN Zhong-sheng, HUANG Heng, WAN Zi-long. The Multi-Behavior Graph Contrastive Learning Recommendation Method with Self-Attention Mechanism[J]. Acta Electronica Sinica, 2024, 52(11): 3684-3698. DOI：10.12263/DZXB.20231084

摘要

图卷积网络因其强大的高阶协作信号学习能力被广泛地应用在多行为推荐系统中.然而，目前大多数基于图卷积的多行为推荐方法未能有效建模不同用户/项目节点与各行为间的关系，且目标行为的稀疏性也困扰着多行为推荐算法性能的进一步提升.基于此，提出一种融合自注意力机制的多行为图对比学习推荐模型（Multi-Behavior Graph Contrastive Learning recommendation method with Self-Attention mechanism， SA-MBGCL）.该方法将用户/项目节点嵌入与行为嵌入相结合，并使用自注意力机制增强嵌入表示，以有效建模不同节点与各行为间的依赖关系.同时，构建一种图对比学习方式，将同一用户的目标行为与辅助行为视为正例对，而不同用户的视为负例对，以强化不同用户的行为差异，达到缓解目标行为稀疏性的目的.将非采样的推荐任务与多行为图对比学习进行多任务联合优化，在Beibei与Taobao这2个公开数据集上，和6个单行为模型与10个多行为模型进行对比，结果表明，所提模型SA-MBGCL在HR（Hit Ratio）和NDCG（Normalize Discounted Cumulative Gain）这2个指标上分别平均提升5.21%和8.30%，说明本文方法是有效的.

Abstract

Graph convolutional network has been widely applied in multi-behavior recommender systems due to its powerful ability to learn high-order collaborative signal. However

most existing graph convolution-based multi-behavior recommendation methods have failed to effectively model the relationships between different user-item nodes and various behaviors. The sparsity of target behaviors also poses challenges to further improve the performance of multi-behavior recommendation algorithms. Based on this

we propose the multi-behavior graph contrastive learning recommendation model with self-attention mechanism (SA-MBGCL). This method combines user-item node embeddings with behavior embeddings and employs a self-attention mechanism to enhance embedding representations

effectively modeling the dependency relationships between different nodes and behaviors. In the meanwhile

a graph contrastive learning approach is constructed

treating the target behavior and auxiliary behaviors of the same user as positive pairs

while considering those of different users as negative pairs

thereby reinforcing behavioral differences among different users to alleviate the sparsity of target behaviors. The proposed model combines unsampled recommendation tasks with multi-behavior graph contrastive learning to perform multi-task joint optimization. It was compared with 6 single-behavior models and 10 multi-behavior models on two public datasets

Beibei and Taobao. The results show that the proposed model SA-MBGCL achieves an average improvement of 5.21% in Hit Ratio (HR) and 8.30% in Normalized Discounted Cumulative Gain (NDCG). This demonstrates the effectiveness of the method presented in this work.

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references

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