  Predicting the next location is a highly valuable and common need in many
location-based services such as destination prediction and route planning. The
goal of next location recommendation is to predict the next point-of-interest a
user might go to based on the user's historical trajectory. Most existing
models learn mobility patterns merely from users' historical check-in sequences
while overlooking the significance of user preference modeling. In this work, a
novel Point-of-Interest Transformer (POIFormer) with contrastive user
preference modeling is developed for end-to-end next location recommendation.
This model consists of three major modules: history encoder, query generator,
and preference decoder. History encoder is designed to model mobility patterns
from historical check-in sequences, while query generator explicitly learns
user preferences to generate user-specific intention queries. Finally,
preference decoder combines the intention queries and historical information to
predict the user's next location. Extensive comparisons with representative
schemes and ablation studies on four real-world datasets demonstrate the
effectiveness and superiority of the proposed scheme under various settings.
