Most existing social recommendation algorithms focus on the user's single interaction such as purchase or click,but do not consider different interactions such as collection and browsing simultaneously.Moreover,current social recommendation algorithms only focus on the accuracy of recommendation,ignoring the interpretability of recommendation results.To solve the above problems,a social recommendation algorithm SRGN is proposed based on graph neural network,which injects the social relationships of users and the objectively existing semantic connections between items into the algorithm architecture in a specific way,and jointly encodes the interactive multi-behavior through message transmission,so as to improve the accuracy of recommendation.In addition,an explainable module is designed to provide reasons for the recommendation results.Compared with other eight algorithms on two real datasets,the results show that the proposed algorithm has obvious advantages in recommendation performance and user friendliness.
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