ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation
This addresses the challenge of improving recommendation accuracy in location-based social networks by modeling periodic patterns, though it is incremental over existing long- and short-term interest models.
The paper tackled the problem of capturing periodic user interests in point-of-interest recommendation by proposing a spatial-temporal periodic interest learning method, achieving state-of-the-art performance on two real-world datasets.
Point-of-Interest (POI) recommendation is an important task in location-based social networks. It facilitates the relation modeling between users and locations. Recently, researchers recommend POIs by long- and short-term interests and achieve success. However, they fail to well capture the periodic interest. People tend to visit similar places at similar times or in similar areas. Existing models try to acquire such kind of periodicity by user's mobility status or time slot, which limits the performance of periodic interest. To this end, we propose to learn spatial-temporal periodic interest. Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest. In the short-term module, we construct various short-term sequences to acquire the spatial-temporal periodic interest of hourly, areal, and hourly-areal granularities, respectively. Finally, we apply inter-level attention to automatically integrate multiple interests. Experiments on two real-world datasets demonstrate the state-of-the-art performance of our method.