Long- and Short-Term Preference Modeling Based on Multi-Level Attention for Next POI Recommendation

The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user's next destination. However, most previous studies have failed to make full use of spatio-temporal information to analyze user check-in periodic regularity, and some studies omit the user's transition preference for the category at the POI semantic level. These are important for analyzing the user's preference for check-in behavior. Long- and short-term preference modeling based on multi-level attention (LSMA) is put forward to solve the above problem and enhance the accuracy of the next POI recommendation. This can capture the user's long-term and short-term preferences separately, and consider the multi-faceted utilization of spatio-temporal information. In particular, it can analyze the periodic hobbies contained in the user's check-in. Moreover, a multi-level attention mechanism is designed to study the multi-factor dynamic representation of user check-in behavior and non-linear dependence between user check-ins, which can multi-angle and comprehensively explore a user's check-in interest. We also study the user's category transition preference at a coarse-grained semantic level to help construct the user's long-term and short-term preferences. Finally, experiments were carried out on two real-world datasets; the findings showed that LSMA modeling outperformed state-of-the-art recommendation systems.

Automated summary

The recommendation of next point-of-interest (POI) is crucial in location-based social networks (LBSNs). Most previous studies have neglected the use of spatio-temporal information and the user's transition preference for POI categories. This research proposes a Long- and Short-term Preference Modeling based on Multi-level Attention (LSMA) to enhance the accuracy of next POI recommendation. The LSMA model can capture the user's long-term and short-term preferences separately and utilize spatio-temporal information comprehensively. Experimental results show that LSMA outperforms state-of-the-art recommendation systems.