基于二部图对比学习的特征增强推荐算法

doi:10.19678/j.issn.1000-3428.0069099

摘要/Abstract

摘要：

信息过载已成为大数据时代面临的一个普遍问题，推荐算法是解决该问题的有效手段。现有的推荐算法具有不同程度的有效性，但仍面临着如何学习更高质量的项目和用户特征以提升推荐性能的挑战。提出一种基于二部图对比学习的特征增强推荐算法FRBGCL。设计一个项目特征初始化模块，利用图卷积网络(GCN)进行各类项目关系二部图的表示学习，并使用基于注意力机制的特征融合策略获取项目初始特征。此外，在构建用户-项目二部图的基础上，设计图对比学习模块进一步增强项目和用户特征，进而提升推荐算法性能。在XuetangX、Last.fm和Yelp2018 3个数据集上的实验结果表明，在选择最优参数的情况下，FRBGCL的Top20推荐结果与次优算法相比，召回率分别提升2.1%、6.8%、11.6%，归一化折损累计增益(NDCG)分别提升1.8%、6.1%、13.1%，命中率(HR)分别提升1.7%、7.8%、8.4%。

关键词: 深度学习, 推荐算法, 二部图, 对比学习, 图卷积网络

Abstract:

Recommendation algorithms are effective in addressing information overload, a common problem in the era of big data. Existing recommendation algorithms have different degrees of effectiveness but still face the challenge of learning higher quality items and user features to enhance recommendation performance. Therefore, this paper proposes a Feature-enhanced Recommendation algorithm based on Bipartite Graph Contrastive Learning (FRBGCL). An item feature initialization module is designed that can use Graph Convolutional Network (GCN) for the representation learning of bipartite graphs of all types of item relationships, and an attention mechanism-based feature fusion strategy is adopted to obtain the initial features of items. In addition, a graph Contrastive Learning (CL) module is designed based on the construction of user-item bipartite graphs, which can further enhance item and user features, leading to an improvement in recommendation performance. On three datasets, XuetangX, Last.fm, and Yelp2018, compared with the suboptimal algorithm, FRBGCL improves the Top20 recommendation results by 2.1%, 6.8%, and 11.6% for recall; 1.8%, 6.1%, and 13.1% for Normalized Discounted Cumulative Gain (NDCG); and 1.7%, 7.8%, and 8.4% for Hit Rate (HR), with optimal parameter selection.

Key words: deep learning, recommendation algorithm, bipartite graph, Contrastive Learning (CL), Graph Convolution Network (GCN)

余鹏, 杨佳琦, 陈欣然, 贺超波. 基于二部图对比学习的特征增强推荐算法[J]. 计算机工程, 2025, 51(7): 100-110.

YU Peng, YANG Jiaqi, CHEN Xinran, HE Chaobo. Feature-enhanced Recommendation Algorithm Based on Bipartite Graph Contrastive Learning[J]. Computer Engineering, 2025, 51(7): 100-110.

https://www.ecice06.com/CN/Y2025/V51/I7/100

图/表 17

图1 FRBGCL模型框架

Fig.1 Framework of FRBGCL model

图2 XuetangX数据集上不同λ₁值的影响

Fig.2 Impact of different λ₁values in XuetangX dataset

图3 Last.fm数据集上不同λ₁值的影响

Fig.3 Impact of different λ₁values on Last.fm dataset

图4 Yelp2018数据集上不同λ₁值的影响

Fig.4 Impact of different λ₁values in Yelp2018 dataset

图5 XuetangX数据集上不同τ值的影响

Fig.5 Impact of different τ values in XuetangX dataset

图6 Last.fm数据集上不同τ值的影响

Fig.6 Impact of different τ values in Last.fm dataset

图7 Yelp2018数据集上不同τ值的影响

Fig.7 Impact of different τ values in Yelp2018 dataset

图8 XuetangX数据集上不同L值的影响

Fig.8 Impact of different L values in XuetangX dataset

图9 Last.fm数据集上不同L值的影响

Fig.9 Impact of different L values in Last.fm dataset

图10 Yelp2018数据集上不同L值的影响

Fig.10 Impact of different L values in Yelp2018 dataset

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