Machine learning

Microeconomics

Sequence (biology)

Task (project management)

Preference

Engineering

Artificial intelligence

Economics

Geometry

Physics

Recommender system

Mathematics

Term (time)

Biology

Systems engineering

Quantum mechanics

Data mining

Recurrent neural network

Point of interest

Computer science

Information retrieval

Point (geometry)

Artificial neural network

Genetics

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation