Mining multiple sequential patterns through multi-graph representation for next point-of-interest recommendation

• Published in: World wide web (Bussum) Vol. 26; no. 4; pp. 1345 - 1370
• Main Authors: Gan, Mingxin, Tan, Caiping
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2023; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Science; Database Management; Graph representations; Graphical representations; Information Systems Applications (incl.Internet); Modules; Operating Systems; Recommender systems; Social networks; Graph convolutional neural networks; Attention networks; Sequential patterns; Next POI recommendation; Multiple influential factors
• ISSN: 1386-145X; 1573-1413
• DOI: 10.1007/s11280-022-01094-3

Next point-of-interest (POI) recommendation is important for users to help them find interesting venues to visit in the near future. Most previous work on this subject has incorporated geographical and temporal information into sequential patterns to predict next POIs. However, few studies have considered the influence of important factors such as users’ reviews or POIs’ popularity on sequential patterns, nor distinguished between factors of different importance for prediction. In addition, the relationships between entities in location-based social networks have been ignored in most previous work. To overcome these limitations, we proposed a model called MGCAN to flexibly incorporate various influential factors into different sequential patterns for next POI recommendation. We first used multiple graph convolutional networks and independent attention networks to model multiple sequential patterns with different influential factors. Furthermore, we designed corresponding modules to simultaneously capture general preferences of users and determine the impact of different influential factors on each user. Finally, we used multiple sequential patterns and the general preferences of users in the prediction module to predict the next POI. Experimental results on two datasets showed that the MGCAN model achieved better recommendation performance than benchmark models.