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A user trajectory simulation framework for next POI recommendation with uncertain check-ins.

Chen Li1, Guoyan Huang1, Shanshan Feng2

  • 1School of Artificial Intelligence, Yanshan University, Qinhuangdao, 066000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 20, 2026
PubMed
Summary

Next point-of-interest (POI) recommendation is improved by simulating user behavior within complex venues. TraSim enhances accuracy by modeling trajectories within collective POIs (CPOIs) and individual POIs (IPOIs).

Keywords:
Monte carlo tree searchNext POI recommendationTrajectory simulationUncertain check-ins

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Area of Science:

  • * Computational Social Science
  • * Data Science
  • * Artificial Intelligence

Background:

  • * Next point-of-interest (POI) recommendation systems struggle with uncertain check-ins, particularly in collective POIs (CPOIs) like shopping malls.
  • * Current methods inadequately represent user behavior within CPOIs, limiting recommendation accuracy.

Purpose of the Study:

  • * To introduce TraSim, a novel user trajectory simulation framework designed to enhance next POI recommendation accuracy.
  • * To address the challenge of unobserved user behaviors within CPOIs by simulating potential user trajectories.

Main Methods:

  • * Utilizes Monte Carlo Tree Search to simulate user trajectories within CPOIs.
  • * Incorporates a heuristic reward module integrating IPOI-level signals (temporal traits, transition patterns, user preferences) and CPOI-level semantic constraints.
  • * Constructs a multi-candidate trajectory pool to ensure simulation robustness and diversity.

Main Results:

  • * TraSim significantly improves recommendation accuracy in uncertain check-in scenarios.
  • * Achieved average improvements of 49.1% in Hit Rate (HR) and 44.0% in Mean Reciprocal Rank (MRR) across three real-world datasets.
  • * Demonstrates effectiveness and efficiency against nine competitive baselines.

Conclusions:

  • * TraSim offers a versatile and scalable solution for next POI recommendation by simulating user behavior within complex venues.
  • * The framework is model-agnostic and hyperparameter-free, functioning as a plug-and-play module for existing recommendation systems.