基于多视图融合跨层对比学习的推荐算法

doi:10.19678/j.issn.1000-3428.0066906

摘要/Abstract

摘要：

现有基于图对比学习的推荐模型在图数据增强方面通常只采用一种视图增强方法，忽略了单一方法存在的局限性，在对比学习方面通常只对比同一节点的一对视图，未充分利用各个视图不同的层嵌入。为此，提出一种基于多视图融合跨层对比学习的推荐算法框架(MFCCL)。MFCCL分别使用随机边丢弃和随机添加噪声的增强方法构建2个全局视图，使用奇异值分解的方法构建局部视图，通过3种不同的视图增强方法构造全局和局部共3个视图，以实现有效的用户表示。同时，提出一种新的多视图融合跨层对比学习方法，该方法将2个全局视图不同的层嵌入通过平行和交叉2种方式进行融合后作对比，以获取更多的特征信息。将多视图融合跨层对比学习与全局-局部视图对比学习相结合，联合优化模型，从而提升推荐性能。在Yelp、Tmall和Amazon-book这3个公开数据集上进行实验，结果表明，MFCCL在推荐任务中具有有效性和可行性，相较于对比模型中性能最优的基线模型SimGCL，MFCCL在3个数据集中的Recall@20增益分别达到15.0%、13.3%和28.7%，NDCG@20值分别提升14.3%、13.2%和29.6%。

关键词: 图神经网络, 对比学习, 视图增强, 多视图融合, 推荐算法

Abstract:

Existing recommendation models based on graph comparison learning usually use only one view enhancement method in graph data enhancement, ignoring the limitations of a single method. In contrastive learning, only a pair of views from the same node are usually compared, which do not fully utilize the different layer embeddings of each view. To this end, this study proposes a recommendation algorithm framework based on Multi-view Fusion Cross-layer Contrastive Learning (MFCCL). MFCCL constructs two global views using random edge drop and random noise addition enhancement methods, local views using Singular Value Decomposition (SVD), and three global and local views using three different view enhancement methods to achieve effective user representation. Simultaneously, a new MFCCL method is proposed, which embeds different layers of two global views through parallel and cross fusion methods for comparison, to obtain more feature information. Combining MFCCL with global-local view contrastive learning aims to jointly optimize the model and improve recommendation performance. Experiments are conducted on three publicly available datasets: Yelp, Tmall, and Amazon-book, and the results demonstrate that MFCCL is effective and feasible in recommendation tasks. Compared to the baseline model SimGCL, which had the best neutral performance in the comparative model, MFCCL exhibited better performance in the three datasets. The Recall@20 gains reached 15.0%, 13.3%, and 28.7%, and the NDCG@20 values increased by 14.3%, 13.2%, and 29.6%, respectively.

Key words: Graph Neural Network(GNN), contrastive learning, view enhancement, multi-view fusion, recommendation algorithm

顾嘉静, 杨丹, 聂铁铮, 寇月. 基于多视图融合跨层对比学习的推荐算法[J]. 计算机工程, 2024, 50(1): 120-128.

Jiajing GU, Dan YANG, Tiezheng NIE, Yue KOU. Recommendation Algorithm Based on Multi-view Fusion Cross-layer Contrastive Learning[J]. Computer Engineering, 2024, 50(1): 120-128.

https://www.ecice06.com/CN/Y2024/V50/I1/120

图/表 10

图1 基于多视图融合跨层对比学习的推荐算法框架

Fig.1 Recommendation algorithm framework based on multi-view fusion cross-layer contrastive learning

图2 多视图融合跨层对比学习

Fig.2 Multi-view fusion cross-layer contrastive learning

图3 数据稀疏性实验结果

Fig.3 Experimental results of data sparsity

图4 Yelp数据集上嵌入分布的T-SNE可视化

Fig.4 T-SNE visualization of embedded distribution on Yelp dataset

图5

$ {\lambda } $

对模型性能的影响

Fig.5 Effect of

$ {\lambda } $

on model performance

图6

$ {\tau } $

对模型性能的影响

Fig.6 Effect of

$ {\tau } $

on model performance

参考文献 34

1	KAPANIPATHI P, THOST V, SANKALP PATEL S, et al. Infusing knowledge into the textual entailment task using graph convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(5): 8074- 8081. doi: 10.1609/aaai.v34i05.6318
2	GALETZKA F, ROSE J, SCHLANGEN D, et al. Space efficient context encoding for non-task-oriented dialogue generation with graph attention transformer[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. [S. l. ]: Association for Computational Linguistics, 2021: 15-26.
3	MOHAMED A, QIAN K, ELHOSEINY M, et al. Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2002.11927.
4	ZHANG Z Q, SHI Y Y, YUAN C F, et al. Object relational graph with teacher-recommended learning for video captioning[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 22-36.
5	潘嘉诚, 董一鸿, 陈华辉. 基于图神经网络的自闭症辅助诊断研究综述. 计算机工程, 2022, 48(9): 1- 11. URL
	PAN J C, DONG Y H, CHEN H H. Review of research on auxiliary diagnosis of autism based on graph neural networks. Computer Engineering, 2022, 48(9): 1- 11. URL
6	DEVLIN J, CHANG M, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1810.04805.pdf.
7	GIDARIS S, SINGH P, KOMODAKIS N. Unsupervised representation learning by predicting image rotations[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1803.07728.
8	CHI P H, CHUNG P H, WU T H, et al. Audio ALBERT: a lite BERT for self-supervised learning of audio representation[C]//Proceedings of 2021 IEEE Spoken Language Technology Workshop. Washington D. C., USA: IEEE Press, 2021: 123-156.
9	WU J C, WANG X, FENG F L, et al. Self-supervised graph learning for recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2021: 726-735.
10	王曙燕, 郭睿涵, 孙家泽. 基于图对比学习的MOOC推荐方法. 计算机工程, 2023, 49(1): 57-64, 72. URL
	WANG S Y, GUO R H, SUN J Z. Recommendation method for MOOC based on graph contrastive learning. Computer Engineering, 2023, 49(1): 57-64, 72. URL
11	QIU J Z, CHEN Q B, DONG Y X, et al. GCC: graph contrastive coding for graph neural network pre-training[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2020: 1150-1160.
12	ZHU Y Q, XU Y C, YU F, et al. Graph contrastive learning with adaptive augmentation[C]//Proceedings of the 2021 Web Conference. New York, USA: ACM Press, 2021: 2069-2080.
13	SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model. IEEE Transactions on Neural Networks, 2009, 20(1): 61- 80. doi: 10.1109/TNN.2008.2005605
14	BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1312.6203.
15	HU W, LIU B, GOMES J, et al. Strategies for pre-training graph neural networks[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1905.12265.pdf.
16	KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1609.02907.pdf.
17	KRASANAKIS E, PAPADOPOULOS S, KOMPATSIARIS I. p2pGNN: a decentralized graph neural network for node classification in peer-to-peer networks. IEEE Access, 2022, 10, 34755- 34765. doi: 10.1109/ACCESS.2022.3159688
18	LIBEN-NOWELL D, KLEINBERG J. The link prediction problem for social networks[C]//Proceedings of the 12th International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2003: 556-559.
19	YING R, YOU J X, MORRIS C, et al. Hierarchical graph representation learning with differentiable pooling[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2018: 4805-4815.
20	TAN Q Y, LIU N H, ZHAO X, et al. Learning to hash with graph neural networks for recommender systems[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2003.01917.
21	HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[EB/OL]. [2023-01-05]. https://arxiv.org/abs/1708.05031.
22	苗雨欣, 宋春花, 牛保宁, 等. 双通道图协同过滤推荐算法. 计算机工程, 2022, 48(8): 121- 128. URL
	MIAO Y X, SONG C H, NIU B N, et al. Dual-channel graph collaborative filtering recommendation algorithm. Computer Engineering, 2022, 48(8): 121- 128. URL
23	XIA X, YIN H Z, YU J L, et al. Self-supervised hypergraph convolutional networks for session-based recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4503- 4511. doi: 10.1609/aaai.v35i5.16578
24	YANG Y H, HUANG C, XIA L H, et al. Knowledge graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 1434-1443.
25	WEI Y W, WANG X, LI Q, et al. Contrastive learning for cold-start recommendation[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2107.05315.
26	HALKO N, MARTINSSON P G, TROPP J A. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 2011, 53(2): 217- 288. doi: 10.1137/090771806
27	CHEN L, WU L, HONG R, et al. Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2001.10167.pdf.
28	HE X H, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2002.02126.
29	WANG X, JIN H Y, ZHANG A, et al. Disentangled graph collaborative filtering[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2007.01764.
30	WANG J L, DING K Z, HONG L J, et al. Next-item recommendation with sequential hypergraphs[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2020: 1101-1110.
31	YU J L, YIN H Z, LI J D, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[EB/OL]. [2023-01-05]. https://arxiv.org/abs/2101.06448.
32	XIA L H, HUANG C, XU Y, et al. Hypergraph contrastive collaborative filtering[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 70-79.
33	XIA L H, HUANG C, ZHANG C X. Self-supervised hypergraph transformer for recommender systems[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2022: 2100-2109.
34	YU J L, YIN H Z, XIA X, et al. Are graph augmentations necessary? simple graph contrastive learning for recom-mendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 1294-1303.

[1]	何杏宇, 周易歆, 罗东旭, 杨桂松. 基于图神经网络和多主体评价的教学资源推荐[J]. 计算机工程, 2024, 50(7): 13-22.
[2]	钱来, 赵卫伟. 基于对比学习和注意力机制的文本分类方法[J]. 计算机工程, 2024, 50(7): 104-111.
[3]	刘娟, 段友祥, 陆誉翕, 张鲁. 引入知识增强和对比学习的知识图谱补全[J]. 计算机工程, 2024, 50(7): 112-122.
[4]	张正康, 杨丹, 聂铁铮, 寇月. 基于图结构聚类的自监督学习疾病诊断方法[J]. 计算机工程, 2024, 50(7): 360-371.
[5]	武星, 殷浩宇, 姚骏峰, 李卫民, 钱权. 面向视频数据的多模态情感分析[J]. 计算机工程, 2024, 50(6): 218-227.
[6]	孙文洁, 李宗民, 孙浩淼. 基于图神经网络的多智能体强化学习值函数分解方法[J]. 计算机工程, 2024, 50(5): 62-70.
[7]	游奔, 李晓红, 姚锦, 冯绍杰. 基于多粒度图与注意力机制的半监督短文本分类[J]. 计算机工程, 2024, 50(5): 83-90.
[8]	张宝鑫, 杨丹, 聂铁铮, 寇月. 基于自监督的多视角图协同过滤推荐方法[J]. 计算机工程, 2024, 50(5): 100-110.
[9]	李田芳, 普园媛, 赵征鹏, 徐丹, 钱文华. 基于CLIP和双空间自适应归一化的图像翻译[J]. 计算机工程, 2024, 50(5): 229-240.
[10]	吴正洋, 张广涛, 黄立, 汤庸. 基于异质图嵌入和会话交互的课程推荐模型[J]. 计算机工程, 2024, 50(4): 95-103.
[11]	王明虎, 石智奎, 苏佳, 张新生. 基于RoBERTa和图增强Transformer的序列推荐方法[J]. 计算机工程, 2024, 50(4): 121-131.
[12]	王华珍, 许泽, 孙悦, 丘斌, 陈坚, 邱强斌. 基于事件演化图的多标记事件预测模型[J]. 计算机工程, 2024, 50(4): 132-140.
[13]	张洪程, 李林育, 杨莉, 伞晨峻, 尹春林, 颜冰, 于虹, 张璇. 基于对比学习与语言模型增强嵌入的知识图谱补全[J]. 计算机工程, 2024, 50(4): 168-176.
[14]	李政学, 李枝名, 彭德中, 陈杰. 基于特征对比学习和图卷积的社交网络用户分类[J]. 计算机工程, 2024, 50(4): 258-266.
[15]	吴冠荣, 李元祥, 王艺霖, 陆雨寒, 陈秀华. 基于对比学习的小样本金属表面损伤分类[J]. 计算机工程, 2024, 50(3): 36-43.

选择文件类型/文献管理软件名称

选择包含的内容