# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
###### Abstract  
Traditional collaborative filtering recommendation algorithms only consider the interaction between users and items leading to low recommendation accuracy. Aiming to solve this problem, a graph convolution collaborative filtering recommendation method integrating social relations is proposed. Firstly, a social recommendation model based on graph convolution representation learning and general collaborative filtering (SRGCF) is constructed; then, based on this model, a social relationship recommendation algorithm (SRRA) is proposed; secondly, the algorithm learns the representations of users and items by linear propagation on the user-item bipartite graph; then the user representations are updated by learning the representations with social information through the neighbor aggregation operation in the social network to form the final user representations. Finally, the prediction scores are calculated, and the recommendation list is generated. The comparative experimental results on four real-world datasets show that: the proposed SRRA algorithm performs the best over existing baselines on Recall@10 and NDCG@10; specifically, SRRA improved by an average of 4.40% and 9.62% compared to DICER and GraphRec, respectively, which validates that the proposed SRGCF model and SRRA algorithm are reasonable and effective.  
social relations; collaborative filtering; graph convolutional network; recommendation system +
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propagation on the user-item graph. It finally computes the weighted sum of embeddings at all layers to produce final embeddings.  
Although LightGCN achieves high levels of accuracy, it only takes data about historical interactions between users and items; many RS can manage a wealth of data describing user-to-user relationships (such as friendship or trust relationships) which are, de facto, ignored by LightGCN. As a consequence, the semantics of information mined by LightGCN is relatively simple and we ask if social data can be profitably integrated in the recommendation task to increase the level of accuracy and, potentially, to make the recommendation process more scalable. Motivated by theories from social science about influence and homophily, many researchers suggested to integrate user-item ratings with information describing social relationships (such as friendship or trust relationships); in fact, users' preferences as well as the decisions they take are often influenced by the behaviors from their peers and, thus, the predictive accuracy of an RS is magnified if social relationships are somewhat taken into account.  
An RS which considers social information is often called social recommender system. Graph theory provide powerful tools to represent user interactions among users as well as interactions between users and items. On one hand, in fact, we could define a user-user interaction network (often called social graph), which maps user onto nodes and in which edges cab represent friendship or trust relationships. On the other hand, we could use a bipartite graph (often called user-item interaction graph) to model users and items as nodes while edges are relevant to encode a variety of interactions such as purchases, clicks, and comments. Several social recommender systems which jointly leverage social graph and interactive graph have been proposed so far. The first is to extend matrix factorization methods into the interactive graph and the social graph, and the second is to apply Graph Neural Networks (in short, GNNs) to obtain meaningful representations.