Robust Preference-Guided Based Disentangled Graph Social Recommendation

Social recommendations introduce additional social information to capture users' potential item preferences, thereby providing more accurate recommendations. However, friends do not always have the same or similar preferences, which means that social information is redundant and often biased for user-item interaction network. In addition, current social recommendation models focus on the item-level preferences, neglecting the critical fine-grained preference influence factors. To address these issues, we propose the Robust Preference-Guided based Disentangled Graph Social Recommendation (RPGD). First, we employ a graph neural network to adaptively convert the social network into a social preference network based on social information and user-item interaction information, reducing bias between social relationships and preference relationships. Then, we propose a self-supervised learning method that utilizes the social network to constrain and optimize the social preference network, thereby enhancing the stability of the network. Finally, we propose a method for disentangled preference representation to explore fine-grained preference influence factors, that enhance the performance of user and item representations. We conducted experiments on some open-source real-world datasets, and the results show that RPGD outperforms the SOTA performance on social recommendations.