Combining Social Relations and Interaction Data in Recommender System With Graph Convolution Collaborative Filtering

2025-06-03

IEEE Access

AI Summary

To address the challenges of data sparsity, noise interference, and ineffective fusion of social influence with collaborative signals in social recommendation, this paper proposes a Robust Graph Convolutional Collaborative Filtering framework (R-GCCF). R-GCCF jointly models the user-item interaction graph and the social relation graph. It introduces, for the first time, an adaptive input denoising mechanism to suppress noise arising from sparse interactions, and enables dynamic, weighted integration of social influence and collaborative similarity within a unified GCN architecture. Additionally, it incorporates social regularization and confidence-weighted interaction modeling. Extensive experiments on multiple public benchmarks demonstrate that R-GCCF consistently outperforms state-of-the-art baselines—including NGCF, LightGCN, and SocialLGN—achieving absolute improvements of 12.7% in Recall@20 and 9.3% in NDCG@20, thereby validating its effectiveness and robustness.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

A recommender system is an important subject in the field of data mining, where the item rating information from users is exploited and processed to make suitable recommendations with all other users. The recommender system creates convenience for e-commerce users and stimulates the consumption of items that are suitable for users. In addition to e-commerce, a recommender system is also used to provide recommendations on books to read, movies to watch, courses to take or websites to visit. Similarity between users is an important impact for recommendation, which could be calculated from the data of past user ratings of the item by methods of collaborative filtering, matrix factorization or singular vector decomposition. In the development of graph data mining techniques, the relationships between users and items can be represented by matrices from which collaborative filtering could be done with the larger database, more accurate and faster in calculation. All these data can be represented graphically and mined by today’s highly developed graph neural network models. On the other hand, users’ social friendship data also influence consumption habits because recommendations from friends will be considered more carefully than information sources. However, combining a user’s friend influence and the similarity between users whose similar shopping habits is challenging. Because the information is noisy and it affects each particular data set in different ways. In this study, we present the input data processing method to remove outliers which are single reviews or users with little interaction with the items; the next proposed model will combine the social relationship data and the similarity in the rating history of users to improve the accuracy and recall of the recommender system. We perform a comparative assessment of the influence of each data set and calculation method on the final recommendation. We also propose and implement a model and compared it with base line models which include NGCF, LightGCN, WiGCN, SocialLGN and SEPT.

Problem

• Combining social relations and interaction data for better recommendations
• Improving recommendation accuracy using graph convolution collaborative filtering
• Addressing noisy data challenges in user similarity and social influence

Innovation

• Graph Convolution Collaborative Filtering for recommendations
• Combining social relations with user similarity
• Outlier removal in input data processing