Region-aware neural graph collaborative filtering for personalized recommendation

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion. Especially, personalized recommendation models based on graph structure have advanced greatly in predicting user preferences. However, geographical region entities that reflect the geographical context of the items is not being utilized in previous works, leaving room for the improvement of personalized recommendation. This study proposes a region-aware neural graph collaborative filtering (RA-NGCF) model, which introduces the geographical regions for improving the prediction of user preference. The approach first characterizes the relationships between items and users with a user-item-region graph. And, a neural network model for the region-aware graph is derived to capture the higher-order interaction among users, items, and regions. Finally, the model fuses region and item vectors to infer user preferences. Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations. This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.