Background: The proliferation of e-commerce has made recommender systems indispensable for navigating vast product catalogs and enhancing user experience. However, conventional recommendation algorithms, such as collaborative and content-based filtering, often operate in a contextual vacuum, overlooking the profound influence of a user's real-world environment on their purchasing decisions. This limitation can lead to generic and suboptimal suggestions, failing to capture nuanced local and demographic preferences.

Purpose: This study aims to design, implement, and evaluate a novel recommendation framework that addresses this gap by explicitly integrating geographic context and population characteristics. The objective is to create a hyper-personalized recommendation engine that provides more accurate, relevant, and contextually-aware product suggestions than traditional models.

Methodology: We propose a Geo-Demographic Recommender Framework (GDRF), a hybrid model that fuses user-item interaction data with two key contextual layers. The geographic component leverages principles from Geographic Information Systems (GIS) to model spatial influences on user preferences. The demographic component performs user segmentation based on population attributes to capture regional lifestyle patterns. The outputs are integrated into a unified scoring function to generate the final recommendations. The framework's performance was evaluated against several baseline models using standard metrics, including Precision, Recall, and NDCG.

Results: Our experimental results demonstrate that the proposed GDRF significantly outperforms traditional recommendation models. The fusion of geospatial and demographic data was associated with a marked improvement in all evaluation metrics, supporting the hypothesis that context is a critical factor in predicting consumer behavior. An ablation study further verified that both the geographic and demographic components are unique and substantial predictors of the model's overall performance.

Conclusion: This research provides a robust framework for enhancing e-commerce recommendations by moving beyond historical interaction data. By systematically incorporating where users live and their surrounding demographic context, e-commerce platforms can offer a superior level of personalization, which is associated with increased user satisfaction and commercial success.