Results 91 to 100 of about 1,557 (194)

The Surprising Bias of PPML Estimates of Structural Gravity Models With Two‐Way Fixed Effects

Review of International Economics, Volume 34, Issue 3, Page 615-631, August 2026.
ABSTRACT The previous literature has shown that the Poisson Pseudo‐Maximum Likelihood (PPML) estimator provides consistent and asymptotically unbiased estimates of the parameters of structural gravity models with two‐way fixed effects, although their standard errors need correction.
Ben Shepherd, Tom Zylkin
wiley   +1 more source

A Deep Reinforcement Learning Methodology to Balance Geoprivacy and Local Search Utility in Location‐Based Services

Transactions in GIS, Volume 30, Issue 5, August 2026.
ABSTRACT The research work addresses the need to preserve the user's privacy in urban contexts without relying on pure contemporary approaches—like k‐anonymity or stochastic noise. We present a privacy preserving methodology that adapts to varying spatial contexts of users.
Omid Reza Abbasi, Franz Welscher, Johannes Scholz   +2 more
wiley   +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

Generative Next POI Recommendation with Semantic ID

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendation methods usually employ random numeric IDs for POIs, limiting the ability to model semantic relationships between similar locations.
Dongsheng Wang, Yuxi Huang, Shen Gao, Yifan Wang, Chengrui Huang, Shuo Shang   +5 more
openaire   +2 more sources

A Tour Recommendation System Considering Implicit and Dynamic Information

Applied Sciences
Tourism has become one of the world’s largest service industries. Due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies.
Chieh-Yuan Tsai, Kai-Wen Chuang, Hen-Yi Jen, Hao Huang   +3 more
doaj   +1 more source

Dual Branch Graph Representation Learning-Based Approach for Next Point-of-Interest Recommendation

IEEE Access
Next Point-of-Interest (POI) recommendation, a sub-task of POI recommendation, focuses on predicting the next POI a user will visit, relying on the user’s sequential check-in history.
Guoning Lv, Min Gao
doaj   +1 more source

Accurate POI recommendation for random groups with improved graph neural networks and a multi-negotiation model. [PDF]

Sci Rep
Song X, Liu Z, Meng L, Chu D, Yu J, Sheng QZ.   +5 more
europepmc   +1 more source

Personalized recommendation of PoIs to people with autism [PDF]

Communications of the ACM, 2022
openaire   +1 more source