E-COMMERCE RECOMMENDATIONS THROUGH GEOGRAPHIC CONTEXT AND POPULATION CHARACTERISTICS
Abstract
Recommender systems are integral to the success of modern e-commerce platforms, guiding users to products and services that align with their preferences. While traditional systems often rely on past purchase behavior or content similarity, the increasing ubiquity of location-based services presents a significant opportunity to infuse geographic context into recommendation logic. This article presents a comprehensive overview of how geographic information, particularly in relation to population characteristics, can enhance e-commerce recommender systems. We explore methodologies for integrating spatial data, discuss the architectural implications, and analyze the benefits and challenges of developing location-aware recommendation strategies. Our review synthesizes existing research on point-of-interest (PoI) recommendations, location-based services, and geospatial information systems (GIS) within e-commerce, highlighting the potential for hyper-personalized experiences and localized business growth. We conclude by outlining key research gaps and future directions for leveraging geographic and demographic data to optimize e-commerce recommendations.
Most read articles by the same author(s)
- Suprajyotsna Dasari , Automated Testing Techniques for Enterprise Software Systems with GenAI Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dwi Jatmiko, Huu Nguyen, AI-Guided Policy Learning For Hyperdimensional Sampling: Exploiting Expert Human Demonstrations From Interactive Virtual Reality Molecular Dynamics , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Sashank Siwakoti, Bhaskar Chaganti, Human-in-the-Loop Control Planes for Cortex Agents: Policy-Driven Escalation, Approval, and Evidence Capture , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Erion Hoxha, Dr. Elira Dervishi, Global Firefly Optimization Model for IoT Attack Detection , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Nethika Perera, Kavindu Jayasinghe, Dynamic Risk-Based Access Control for Autonomous Agentic AI Systems: Architecture, Policy Enforcement, and Security Evaluation , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Kolchin Rustam, Development and Implementation of the Mail Security Guardian (MSG) System for Multi-Layer Proactive Email Protection Against Spam, Phishing and Malware , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Nadezhda Shiroglazova, Dynamic Operator Allocation for Conversational AI Voice-Calling Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Mohammed Imran Choudhary, AI-Augmented Network-Forensics: Leveraging LLMs for Real-Time Threat Detection and Automated Response in Enterprise Environments , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Myroslav Mishov, Autonomous Threat Remediation in Localized AI Environments: A Review of Security-as-Code Execution Models , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Prof. Robert J. Mitchell, EVALUATING A FOUNDATIONAL PROGRAM FOR CYBERSECURITY EDUCATION: A PILOT STUDY OF A 'CYBER BRIDGE' INITIATIVE , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 03 (2025): Volume 02 Issue 03