# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
## 6 Conclusions  
In this work, we proposed a new social recommendation method which leverages graph convolution technique and integrates social relations. Firstly, we construct the architecture of a general collaborative filtering social recommendation model based on graph convolution (SRGCF), which consists of four parts, which are initialization embedding layer, semantic aggregation layer, semantic fusion layer and prediction layer, respectively. The semantic aggregation layer and semantic fusion layer are the core of SRGCF, which play the role of extracting high-order semantic information and integrating various semantic information, respectively. Then, we propose a feasible SRRA algorithm on top of the architecture, which can model interactions as well as social relations. It can use richer social information to mine the potential relationship, so as to improve the performance of recommendations. Comparative experiments on four datasets have proven the effectiveness of the proposed model. Different from previous work, we try to explore how to use graph neural network method and introduce social auxiliary information to construct recommendation model in order to learn better representation. The graph-based model is superior to the traditional recommendation model because it can learn not only the representation of entities but also the relationships between them. However, limited by the shortcomings of graph neural network itself, such as excessive smoothing after several iterations, entity representation may not be fully learned, which requires some optimization in model design. In the future, we plan to optimize the model architecture by increasing the coupling between social modeling and interactive modeling, so that the representation learning is more adequate. We will also try to explore the advantages of other graphical representation learning techniques to improve the learning ability of the model. Conceptualization, M.M. ; methodology, X.L. ; software, Q.C. ; formal analysis, Q.C. ; writing--original draft preparation, Q.C. ; writing--review and editing, Q.C. All authors have read and agreed to the published version of the manuscript. Figure 8: Effect of embedding size \(d\) on Gowalla and Epinions. **Funding:** This research was funded by Science and Technology Research Project of Chongqing Municipal Education Commission, grant number KJZDK202001101; Chongqing Postgraduate Research Innovation Project, grant number gzlcx20223205; General Project of Chongqing Natural Science Foundation, grant number cstc2021 jcyj-msxmX0162; 2021 National Education Examination Research Project, grant number GJK2021028. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The calculated data presented in this work are available from the corresponding authors upon reasonable request. **Acknowledgments:** The author would like to thank the anonymous reviewers for their valuable comments on our paper. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**