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<p>Point-of-Interest (POI) recommendations in Location-Based Social Networks (LBSNs) are susceptible to data sparsity, dynamic user preferences, and the need to strike a balance between exploration and exploitation. In this paper, we present a Multi-Agent Hierarchical Q-Learning (MAHQL) framework that combines adversarial learning, meta-learning, and multi-agent coordination to overcome these issues. The three-phase model first captures user preferences across global, local, and temporal dimensions, thereby addressing data sparsity and cold-start issues. The second phase posits a Q-policy generator that integrates the multi-agent coordination with adaptive exploration strategies to strike a balance between exploration and exploitation. In the third phase, a meta-learning–enhanced hierarchical discriminator provides diverse recommendations that reduce the echo-chamber effect. MAHQL has been tested on real-world datasets: Gowalla, Foursquare, and Weeplaces. It has achieved 71.84% accuracy on Gowalla, 75.74% on Foursquare, and 68.00% on Weeplaces, and the performance has been juxtaposed to several baselines. The results suggest improved performance and user satisfaction.</p>

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Multi-agent hierarchical Q-learning framework for point-of-interest recommendation in location-based social networks

  • 期刊论文

  • 发表日期：

    2026年6月28日

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  • Malika Acharya,
  • Krishna Kumar Mohbey

摘要

Point-of-Interest (POI) recommendations in Location-Based Social Networks (LBSNs) are susceptible to data sparsity, dynamic user preferences, and the need to strike a balance between exploration and exploitation. In this paper, we present a Multi-Agent Hierarchical Q-Learning (MAHQL) framework that combines adversarial learning, meta-learning, and multi-agent coordination to overcome these issues. The three-phase model first captures user preferences across global, local, and temporal dimensions, thereby addressing data sparsity and cold-start issues. The second phase posits a Q-policy generator that integrates the multi-agent coordination with adaptive exploration strategies to strike a balance between exploration and exploitation. In the third phase, a meta-learning–enhanced hierarchical discriminator provides diverse recommendations that reduce the echo-chamber effect. MAHQL has been tested on real-world datasets: Gowalla, Foursquare, and Weeplaces. It has achieved 71.84% accuracy on Gowalla, 75.74% on Foursquare, and 68.00% on Weeplaces, and the performance has been juxtaposed to several baselines. The results suggest improved performance and user satisfaction.