AFGSRec: A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features

Previous session-based recommendation systems usually capture users’ consumption preferences from their recent transaction records, and this method ignores the influence of global transaction information and friends’ preferences on users’ transaction behavior, resulting in less accurate recommendation results of the model. To solve the problem, this paper proposed a social recommendation model AFGSRec based on an adaptive fusion of global collaborative features. Firstly, a heterogeneous graph neural network was used to model users and their historical transaction information on the social network for capturing global collaborative features and social influence among friends. Secondly, this paper designed a graph neural network based on a selection mechanism that effectively filters out the node transition features irrelevant to the current session and captures user preferences more accurately. Thirdly, an adaptive fusion method was designed to capture the impact of global collaborative features on users’ current preferences dynamically and improve the model’s recommendation accuracy. Finally, this paper used a dynamic cyclical learning rate to help the model better handle saddle points during the training process to improve the convergence speed of model AFGSRec. The experimental results show that AFGSRec is robust; both the HR (Hit Rate) and MRR (Mean Reciprocal Rank) of AFGSRec outperform the state-of-art model SERec. On the Gowalla dataset, HR@10 and HR@20 are increased by 1.91% and 1.15%, respectively; MRR@10 and MRR@20 are increased by 5.05% and 4.83%, respectively. On the Delicious dataset, HR@10 and HR@20 are increased by 2.45% and 1.19%, respectively; MRR@10 and MRR@20 are increased by 4.84% and 4.32%, respectively.