Utilization of Spatio-Temporal and Social Information for POI Group Recommendation

• Main Authors: Niu, Pengyu, Qu, Boting, Feng, Jun, Wang, Xin
• Format: Conference Proceeding
• Language: English
• Subjects: Aggregates; Attention network; Decision making; Fuses; Geospatial analysis; Location-Based Social Networks; POI group recommendation; Social networking (online); User experience
• ISSN: 2375-0324
• DOI: 10.1109/MDM58254.2023.00017

POI group recommendation offers a list of locations to a group of users based on their visiting preferences, which is crucial for Location-Based Social Networks(LBSNs) to improve user experience quality and group satisfaction. Current studies either regard the POI as a general item for group recommendation, or do not make full use of the geospatial information of POIs and users' social friends' information to generate the group visiting preference representations. In this paper, a neural network-based POI group recommendation method named STSPGR is proposed. STSPGR first learns the user embedding vector that fuses temporal, spatial, categorical, and social information to represent each user's visiting preference. Then it utilizes the attention network to dynamically learn the impact degrees of each member in the group decision-making process to aggregate the group visiting preference embedding. Finally, the POI recommender decodes the group's embedding to the preference scores over all POIs to make the recommendation. Experiments are conducted on three real-world datasets, which show that the proposed STSPGR has better recommendation accuracy than other POI group recommendation methods.