## 1 Introduction  
E-commerce encompasses the buying and selling of goods and services over the Internet [1]. While traditional e-commerce systems have focused primarily on transaction processing and basic recommendation systems, the integration of Geographic Information Systems (GISs) and mobile agents is opening new possibilities for enhanced customer experiences and personalized service delivery. Geographic Information Systems (GISs) are specialized software systems designed to capture, store, manipulate, analyze, manage, and present various types of spatial or geographical data [2]. In e-commerce, GISs can provide crucial insights into customer behavior and market dynamics by analyzing spatial relationships between customers, products, and services. Over the past three decades,researchers have studied the application of GISs in e-commerce. However, a significant challenge in modern e-commerce is the ability to provide real-time, location-aware recommendations that adapt to changing customer contexts and preferences. Traditional recommendation systems often fail to incorporate geographical context and real-time customer behavior effectively. As a result of the decade-long surge in network technology and the widespread use of the Internet and World Wide Web (WWW) [3], GISs (Geographic Information Systems) have seen a shift from the initial closed and centralized model of information management to an open model, which allows the vast amount of data managed by the many different types of GISs located throughout the world to be viewed by the public via the Internet at nearly every location [4]. Furthermore, modern GISs' performance depends on consistent network connectivity and a large amount of transmission capacity due to the prevalent Client/Server design [5]. The issue stems from the fact that the widely used distributed computing paradigm and GIS design could not effectively handle the demands of the rapidly developing application sectors, such as mobile computing, intelligent search, active services, and others, nor adapt to the Internet environment [6]. For distributed GISs in the context of the Internet, such a scenario necessitates the development of novel architecture and distributed computing technologies. A relatively new paradigm in computing called a mobile agent is ideal for open and constantly changing settings since it blends distributed and agent technologies [7]. If a distributed GIS is used in an Internet setting with intermittent and constrained bandwidth for communication, this might be a useful method of introducing a mobile agent computing paradigm [8]. A novel approach to tailored offers is the integration of mobile agents with Geographic Information Systems (GISs) in the context of e-commerce [9]. Engaging in autonomous activities, mobile agents on systems like Aglet or JADE can actively gather data in real time while roaming the network [10]. These agents take into account the user's existing environment and serve as adaptable intermediaries between particular client preferences and the accessible product catalogue [11; 12]. GISs give retailers more insight into the factors influencing customer preferences and behavior through data [13]. They may help them understand why transactions occur where they do by mapping a location against transactions and then aggregating the transactions to find patterns in the data. Location intelligence helps businesses make better decisions about inventory, sales, and marketing by helping merchants understand their customers. In terms of providing region-specific offers and tailored ads, traditional geolocation-based systems have advanced significantly. In order to customize marketing campaigns according to users' locations, platforms such as Google Ads and Facebook Ads employ geolocation data, which include GPS, Wi-Fi, and IP-based monitoring. These systems' dependence on static geolocation data, which ignore real-time location changes and the dynamic nature of client activity, is a drawback.