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

TLDR

POI recommendation is a popular research area that uses users’ check‑in sequences, yet current RNN‑based methods fail to capture long‑term preferences or geographic relations among recent visits, limiting recommendation reliability. We propose Long‑and‑Short‑Term Preference Modeling (LSTPM) to address these limitations. LSTPM uses a nonlocal network to model long‑term preference and a geo‑dilated RNN to learn short‑term preference. Experiments on two real‑world datasets show that LSTPM significantly outperforms state‑of‑the‑art methods.

Abstract

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.