ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation

TL;DR

This paper introduces ST-PIL, a novel method for POI recommendation that effectively captures spatial-temporal periodic interests by integrating multiple interest levels, outperforming existing models on real-world datasets.

Contribution

The paper proposes a new spatial-temporal periodic interest learning framework that models periodicity at multiple granularities and integrates them using attention mechanisms.

Findings

Achieves state-of-the-art performance on two real-world datasets.

Effectively captures periodic interests at hourly, areal, and daily levels.

Outperforms existing methods in POI recommendation accuracy.

Abstract

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.