SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering
With the flourishing of social media platforms, data in social networks, especially user-generated content, are growing rapidly, which makes it hard for users to select relevant content from the overloaded data. Recommender systems are thus developed to filter user-relevant content for better user e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/12070 |
| _version_ | 1826918141740974080 |
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| author | Heng Lu Ziwei Chen |
| author_facet | Heng Lu Ziwei Chen |
| author_sort | Heng Lu |
| collection | DOAJ |
| description | With the flourishing of social media platforms, data in social networks, especially user-generated content, are growing rapidly, which makes it hard for users to select relevant content from the overloaded data. Recommender systems are thus developed to filter user-relevant content for better user experiences and also the commercial needs of social platform providers. Graph neural networks have been widely applied in recommender systems for better recommendation based on past interactions between users and corresponding items due to the graph structure of social data. Users might also be influenced by their social connections, which is the focus of social recommendation. Most works on recommendation systems try to obtain better representations of user embeddings and item embeddings. Compared with recommendation systems only focusing on interaction graphs, social recommendation has an additional task of combining user embedding from the social graph and interaction graph. This paper proposes a new method called SocialJGCF to address these problems, which applies Jacobi-Polynomial-Based Graph Collaborative Filtering (JGCF) to the propagation of the interaction graph and social graph, and a graph fusion is used to combine the user embeddings from the interaction graph and social graph. Experiments are conducted on two real-world datasets, epinions and LastFM. The result shows that SocialJGCF has great potential in social recommendation, especially for cold-start problems. |
| first_indexed | 2025-02-17T12:45:28Z |
| format | Article |
| id | doaj.art-01d2cf26d6f7409c9bf26dbeb51dfdff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2025-02-17T12:45:28Z |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-01d2cf26d6f7409c9bf26dbeb51dfdff2024-12-27T14:09:18ZengMDPI AGApplied Sciences2076-34172024-12-0114241207010.3390/app142412070SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative FilteringHeng Lu0Ziwei Chen1Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USADepartment of Electronics, Beijing Jiaotong University, Beijing 100044, ChinaWith the flourishing of social media platforms, data in social networks, especially user-generated content, are growing rapidly, which makes it hard for users to select relevant content from the overloaded data. Recommender systems are thus developed to filter user-relevant content for better user experiences and also the commercial needs of social platform providers. Graph neural networks have been widely applied in recommender systems for better recommendation based on past interactions between users and corresponding items due to the graph structure of social data. Users might also be influenced by their social connections, which is the focus of social recommendation. Most works on recommendation systems try to obtain better representations of user embeddings and item embeddings. Compared with recommendation systems only focusing on interaction graphs, social recommendation has an additional task of combining user embedding from the social graph and interaction graph. This paper proposes a new method called SocialJGCF to address these problems, which applies Jacobi-Polynomial-Based Graph Collaborative Filtering (JGCF) to the propagation of the interaction graph and social graph, and a graph fusion is used to combine the user embeddings from the interaction graph and social graph. Experiments are conducted on two real-world datasets, epinions and LastFM. The result shows that SocialJGCF has great potential in social recommendation, especially for cold-start problems.https://www.mdpi.com/2076-3417/14/24/12070graph neural networksocial recommendationrecommender systemgraph collaborative filtering |
| spellingShingle | Heng Lu Ziwei Chen SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering Applied Sciences graph neural network social recommendation recommender system graph collaborative filtering |
| title | SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering |
| title_full | SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering |
| title_fullStr | SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering |
| title_full_unstemmed | SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering |
| title_short | SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering |
| title_sort | socialjgcf social recommendation with jacobi polynomial based graph collaborative filtering |
| topic | graph neural network social recommendation recommender system graph collaborative filtering |
| url | https://www.mdpi.com/2076-3417/14/24/12070 |
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