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.
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