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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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A user behavior inertia based spatio temporal next POI recommendation model.

Kaiqi Zhang1, Dianhui Chu2, Zhiying Tu2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China. zhang_kaiqi2024@126.com.

Scientific Reports
|April 2, 2026
PubMed
Summary

This study introduces a novel point-of-interest (POI) recommendation model that accounts for user behavior inertia and resistance factors. The model enhances recommendation accuracy by predicting user purpose and POI check-in probabilities, improving recall and MAP performance.

Keywords:
Behavior inertiaNext POI recommendationSpatial and temporal

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Data Science

Background:

  • Next point-of-interest (POI) recommendation is complex due to user behavior personalization and uncertainty.
  • User behavior exhibits patterns, termed behavior inertia, influencing decision-making.
  • Real-world factors, termed inertia resistance, modulate the impact of behavior inertia.

Purpose of the Study:

  • To develop an accurate POI recommendation model by integrating behavior inertia and inertia resistance.
  • To enhance POI recommendation accuracy by addressing personalization and uncertainty in user behavior.
  • To improve the prediction of the next POI a user will visit.

Main Methods:

  • Classified POIs by user behavior purpose to construct a purpose prediction model.
  • Developed a POI spatial attribute prediction model to calculate check-in probabilities based on purpose.
  • Created a user behavior inertia prediction model incorporating time, check-in counts, and category diversity.

Main Results:

  • The proposed model combines purpose and behavior inertia predictions to estimate the probability of a user visiting a POI.
  • Extensive experiments on two real-world datasets validated the model's effectiveness.
  • The method achieved up to 15% improvement in recall and 20% in Mean Average Precision (MAP) compared to baseline methods.

Conclusions:

  • The novel POI recommendation model effectively leverages behavior inertia and inertia resistance for improved accuracy.
  • The integration of purpose prediction and spatial attributes enhances the understanding of user location-based decisions.
  • The findings suggest a significant advancement in personalized and context-aware POI recommendation systems.