Knowledge-Aware Graph Self-Supervised Learning for Recommendation
Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researc...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/23/4869 |
| _version_ | 1797400259236724736 |
|---|---|
| author | Shanshan Li Yutong Jia You Wu Ning Wei Liyan Zhang Jingfeng Guo |
| author_facet | Shanshan Li Yutong Jia You Wu Ning Wei Liyan Zhang Jingfeng Guo |
| author_sort | Shanshan Li |
| collection | DOAJ |
| description | Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph self-supervised learning (SSL) techniques into CF to alleviate the sparse supervision problem. The technique first augments the data to obtain contrastive views and then utilizes the mutual information maximization to provide self-supervised signals for the contrastive views. However, the existing approaches based on graph self-supervised signals still face the following challenges: (i) Most of the works fail to effectively mine and exploit the supervised information from the item knowledge graph, resulting in suboptimal performance. (ii) Existing data augmentation methods are unable to fully exploit the potential of contrastive learning, because they primarily focus on the contrastive view of data structure changes and neglect the adjacent relationship among users and items. To address these issues, we propose a novel self-supervised learning approach, namely Knowledge-aware Graph Self-supervised Learning (KGSL). Specifically, we calculate node similarity based on semantic relations between items in the knowledge graph to generate a semantic-based item similarity graph. Then, the self-supervised learning contrast views are generated from both the user–item interaction graph and the item similarity graph, respectively. Maximization of the information from these contrastive views provides additional self-supervised signals to enhance the node representation capacity. Finally, we establish a joint training strategy for the self-supervised learning task and the recommendation task to further optimize the learning process of KGSL. Extensive comparative experiments as well as ablation experiments are conducted on three real-world datasets to verify the effectiveness of KGSL. |
| first_indexed | 2024-03-09T01:52:56Z |
| format | Article |
| id | doaj.art-75ca934ab6f841619b27595e3d50b5a3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T01:52:56Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-75ca934ab6f841619b27595e3d50b5a32023-12-08T15:14:18ZengMDPI AGElectronics2079-92922023-12-011223486910.3390/electronics12234869Knowledge-Aware Graph Self-Supervised Learning for RecommendationShanshan Li0Yutong Jia1You Wu2Ning Wei3Liyan Zhang4Jingfeng Guo5College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph self-supervised learning (SSL) techniques into CF to alleviate the sparse supervision problem. The technique first augments the data to obtain contrastive views and then utilizes the mutual information maximization to provide self-supervised signals for the contrastive views. However, the existing approaches based on graph self-supervised signals still face the following challenges: (i) Most of the works fail to effectively mine and exploit the supervised information from the item knowledge graph, resulting in suboptimal performance. (ii) Existing data augmentation methods are unable to fully exploit the potential of contrastive learning, because they primarily focus on the contrastive view of data structure changes and neglect the adjacent relationship among users and items. To address these issues, we propose a novel self-supervised learning approach, namely Knowledge-aware Graph Self-supervised Learning (KGSL). Specifically, we calculate node similarity based on semantic relations between items in the knowledge graph to generate a semantic-based item similarity graph. Then, the self-supervised learning contrast views are generated from both the user–item interaction graph and the item similarity graph, respectively. Maximization of the information from these contrastive views provides additional self-supervised signals to enhance the node representation capacity. Finally, we establish a joint training strategy for the self-supervised learning task and the recommendation task to further optimize the learning process of KGSL. Extensive comparative experiments as well as ablation experiments are conducted on three real-world datasets to verify the effectiveness of KGSL.https://www.mdpi.com/2079-9292/12/23/4869self-supervised learningknowledge graphsemantic similarityrecommendation |
| spellingShingle | Shanshan Li Yutong Jia You Wu Ning Wei Liyan Zhang Jingfeng Guo Knowledge-Aware Graph Self-Supervised Learning for Recommendation Electronics self-supervised learning knowledge graph semantic similarity recommendation |
| title | Knowledge-Aware Graph Self-Supervised Learning for Recommendation |
| title_full | Knowledge-Aware Graph Self-Supervised Learning for Recommendation |
| title_fullStr | Knowledge-Aware Graph Self-Supervised Learning for Recommendation |
| title_full_unstemmed | Knowledge-Aware Graph Self-Supervised Learning for Recommendation |
| title_short | Knowledge-Aware Graph Self-Supervised Learning for Recommendation |
| title_sort | knowledge aware graph self supervised learning for recommendation |
| topic | self-supervised learning knowledge graph semantic similarity recommendation |
| url | https://www.mdpi.com/2079-9292/12/23/4869 |
| work_keys_str_mv | AT shanshanli knowledgeawaregraphselfsupervisedlearningforrecommendation AT yutongjia knowledgeawaregraphselfsupervisedlearningforrecommendation AT youwu knowledgeawaregraphselfsupervisedlearningforrecommendation AT ningwei knowledgeawaregraphselfsupervisedlearningforrecommendation AT liyanzhang knowledgeawaregraphselfsupervisedlearningforrecommendation AT jingfengguo knowledgeawaregraphselfsupervisedlearningforrecommendation |