Local and Global Exploration for Next New POI Recommendation

The next point-of-interest (POI) recommendation is a hot-spot for both industry and academia, which helps users better experience the physical world. However, existing methods suffer from a severe bias towards recommending repeat POIs that have been visited by the target user before, and perform inefficiently when recommending new POIs that have not been visited by the target user yet. To overcome this issue, we delve into the next new POI recommendation and uncover the coexistence of local and global exploration patterns in users’ visits to new POIs, showing their willingness to explore not only nearby new POIs but also those distant ones. Subsequently, we develop a novel L ocal and G lobal E xploration framework (LGE) for the next new POI recommendation. In particular, LGE involves three key modules: 1) a Zone-Aware Local Exploration (ZLE) module, which encourages users to explore POIs in the local area by learning zone-aware POI representations and regularizing POI prediction with zone information; 2) an Intention-Aware Global Exploration (IGE) module, which recommends POIs that meet user intentions without distance constraints by extracting static and dynamic intentions from category information; 3) a fusion module, which contains a Mean Pooling (MP) strategy and a Weighted Pooling (WP) strategy to aggregate the outputs of local and global exploration modules for the final recommendation. Experiments carried out on real-world datasets have shown the effectiveness of LGE in recommending new POIs.