End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling

by   Yan Luo, et al.
The Hong Kong Polytechnic University

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.


Origin-Aware Next Destination Recommendation with Personalized Preference Attention

Next destination recommendation is an important task in the transportati...

A Diffusion model for POI recommendation

Next Point-of-Interest (POI) recommendation is a critical task in locati...

Timestamps as Prompts for Geography-Aware Location Recommendation

Location recommendation plays a vital role in improving users' travel ex...

DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation

Trip itinerary recommendation finds an ordered sequence of Points-of-Int...

Joint Training Capsule Network for Cold Start Recommendation

This paper proposes a novel neural network, joint training capsule netwo...

Construction and Adaptability Analysis of User's Preference Models Based on Check-in Data in LBSN

With the widespread use of mobile phones, users can share their location...

TribeFlow: Mining & Predicting User Trajectories

Which song will Smith listen to next? Which restaurant will Alice go to ...

Please sign up or login with your details

Forgot password? Click here to reset