Results 71 to 80 of about 1,026 (182)

A hybrid system for personalized next point-of-interest recommendations using deep learning and fuzzy logic [PDF]

PeerJ Computer Science
We propose TourOptiGuide, a hybrid tourism recommendation prototype that integrates deep learning and fuzzy logic for context-aware point-of-interest (POI) suggestion. The system generates next-POI recommendations by jointly considering four factors: the
Intissar Hilali, Nouha Arfaoui, Amel Ksibi, Ridha Ejbali   +3 more
doaj   +2 more sources

Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation

CoRR
Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment. Existing studies rely on building recommendation models from large-scale users' check-in data, which is task-specific and needs extensive computational resources.
Shanshan Feng 0001, Haoming Lyu, Caishun Chen, Yew-Soon Ong   +3 more
openaire   +3 more sources

DCal-Rec: A Spatio-Temporal Distribution Calibration Framework for Next-POI Recommendation

ISPRS International Journal of Geo-Information
The rapid expansion of mobile user behavior data has made next-Point-of-Interest (POI) recommendation increasingly vital for enhancing personalized location-based services.
Meihui Shi, Peng Zhang, Jinlian Du, Zhi Cai   +3 more
doaj   +1 more source

GeoMamba: Toward Efficient Geography-Aware Sequential POI Recommendation

IEEE Access
“Where to go next” is the fundamental problem in sequential point-of-interest (POI) recommendation, which takes as input the individual check-in history, mines the dynamic preference and suggests the expected POI for the next step behavior.
Jiubing Chen, Haoyu Wang, Jianxin Shang
doaj   +1 more source

Next Location Recommendation with Bi-Directional Inference and Similarity-Aware Enhancement

Applied Sciences
The greatest challenge of location recommendation is data sparsity. Some works introduce auxiliary information such as social links and semantics to enrich the representation. However, there are still two main limitations.
Xixi Li, Kaijun Miao
doaj   +1 more source

Point of Interest Recommendation System Using Sentiment Analysis

Journal of Information Science Theory and Practice
Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback.
Gaurav Meena, Ajay Indian, Krishna Kumar Mohbey, Kunal Jangid   +3 more
doaj   +1 more source

RETRACTED: Next Point of Interest (POI) Recommendation System Driven by User Probabilistic Preferences and Temporal Regularities

Mathematics
The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent ...
Fengyu Liu, Jinhe Chen, Jun Yu, Rui Zhong   +3 more
doaj   +1 more source

Hypergraph User Embeddings and Session Contrastive Learning for POI Recommendation

IEEE Access
Internet technologies have enabled location-based social networks (LBSNs) to provide users with a variety of services. In this context, next Point-of-Interest (POI) recommendation has become a key task.
Yan Zhang, Bin Wang, Qian Zhang, Sulei Zhu, Yan Ma   +4 more
doaj   +1 more source

NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation

Information
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs)
Hongwei Zhang, Guolong Wang, Xiaofeng Yan   +2 more
doaj   +1 more source

Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation

ISPRS International Journal of Geo-Information
Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation ...
Chenglin Yu, Lihong Shi, Yangyang Zhao
doaj   +1 more source