## 1 Introduction
Even though traditional e-commerce platforms, similar to those used by Amazon and Walmart, have incorporated geolocation for targeted reduction and regional inventory operation, they still struggle to give recommendations that are really adapted and contextually aware [14]. The capabilities of mobile agents and GIS technologies are combined in our suggested framework to provide a more flexible and dynamic solution. Real-time location information and autonomous mobile agents are incorporated in our system to provide tailored recommendations that adapt to the changing experience and activity of customers. To provide timely and relevant product suggestions, our system leverages both geographical and temporal data, in contrast to existing solutions that frequently depend on static data and lack adaptability. Its dynamic flexibility makes it more suitable for the needs of contemporary e-commerce and marks a substantial improvement over conventional geolocation-based solutions [15]. This research proposes an innovative framework that leverages advanced geospatial technologies and mobile agent systems to transform e-commerce recommendation systems. By integrating real-time location data with autonomous mobile agents, our approach aims to address the fundamental challenges in delivering personalized, context-aware shopping experiences. Through a comprehensive analysis of existing e-commerce systems and their limitations in handling spatial data, we have identified three critical research questions:  
1. \"How can mobile agents be effectively integrated with GISs to create a real-time, location-aware recommendation system that improves store recommendation accuracy in e-commerce?\"
2. \"What impact does the combination of geographic distance calculations and user ratings have on the quality of store recommendations in a mobile agent-based e-commerce system?\"
3. \"How can a temporal analysis of customer-product interactions be integrated with spatial data to enhance the precision of retail store recommendations?\"  
To address these questions, we have developed a comprehensive framework that combines mobile agent architecture with GIS capabilities, specifically designed for modern e-commerce environments. Our approach not only answers these fundamental questions but also makes several key technical contributions that advance the field of location-aware recommendation systems:  
* The development of a comprehensive framework that integrates GIS capabilities with e-commerce platforms through mobile agents, enabling a new generation of spatially aware recommendation systems;
* The development of a novel scoring system that combines Haversine formula-based distance calculations with user ratings (\(\delta\) = 0.7 and \(\sigma\) = 0.3) for optimal store recommendations;
* The implementation of a multi-agent framework using JADE that integrates six specialized agents for different aspects of the recommendation process;
* The creation of a temporal analysis system for tracking and analyzing customer-product interactions within physical retail spaces;
* The integration of multiple GIS databases with mobile agents to provide real-time location-aware recommendations for retail stores;
* The validation of the framework through a real-world case study with Carrefour and Monoprix, demonstrating an improved recommendation accuracy. This paper is organized as follows. In Section 2, we present definitions for the technology used: GISs and mobile agents. Section 3 introduces the related works, in which the main references for the topic are displayed; Section 4 presents the methodology of the proposed solution adopted in this paper. Section 5 details the experimental results and the main results of a web application of this approach. Discussion is provided in Section 6. Section 7 addresses the limitations of the study. Finally, Section 8 concludes the paper and outlines potential future directions of the research