## 3 Related Works
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Article** & **Approach** & **Multiple** & **Cloud Boot-Up** & **Implementation** & **Adaptation** \\ \hline
[22] & Location-Based HLCP & No & No & Moderate & No \\
[23] & A location-based recommender for & Yes & Yes & High & No \\
[24] & Decation-Based Service Experience & No & No & Moderate & No \\
[25] & Location and Content-Based Approach & Yes & No & Moderate & Yes \\  & The strategy minimizes transportation & & & & \\
[26] & costs and maximizes logistics in forest & & & & \\  & supply chains by utilizing GIS and & & & & Moderate & No \\  & agent-based modeling. & & & & \\  & The method uses a variety of data sources & & & & \\
[27] & to combine GIS with Agent-Based & & No & Yes & Moderate & Yes \\  & Modeling for efficient deployment & & & & \\  & and adaption. & & & & \\  & The strategy uses GIS and business & & & & \\
[28] & analytics to enhance Coffee Refresh’s & No & No & Moderate & Yes \\  & franchise approval choices. & & & & \\
[29] & The method simplifies intricate spatial & & & & \\  & modeling by utilizing GAMA. & & Yes & No & High & Yes \\  & The method uses artificial intelligence (AI) & & & & \\
[30] & to develop a productive 3D landscape & Yes & Yes & Moderate & No \\  & architectural visualization system. & & & & \\  & The method analyzes user reviews of & & & & \\
[31] & locations to improve geographic & No & No & High & Yes \\  & recommendation systems using sentiment & & & & \\  & analysis. & & & & \\  & The study uses GIS and Agent-Based & & & & \\
[32] & Modelling to analyze Mesolithic & & & & \\  & movement and identify archaeological & & & & \\  & potential in Orkney. & & & & \\  & This paper proposes a spatio-temporal & & & & \\
[33] & semantic IoT framework to enhance data & & & & \\  & interoperability and decision-making in & & & & \\  & precision agriculture. & & & & \\ Proposed & Mobile Agent and GIS-Based E-commerce & & & & \\ solution & Application & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative assessment in connection with relevant research in the field. ing intricate spatiotemporal queries and smooth service interoperability with external agricultural data models. According to the literature study, e-commerce has made extensive use of tried-and-true techniques including content-based filtering, collaborative filtering, and hybrid recommendation systems for product suggestions. These approaches, however, frequently fall short in meeting the dynamic, real-time requirements of contemporary e-commerce settings. Although separate research has shown the advantages of mobile agent technologies [24, 27] and GIS integration in recommendation systems [23, 29], there is a clear research vacuum in which both strategies are coupled to provide an adaptive, real-time, location-aware recommendation system. As Table 1 illustrates, existing methods often concentrate on static location data [22, 25] or struggle with limited flexibility since mobile agents are not included. Furthermore, these systems frequently have trouble managing several data sources efficiently while being responsive, which is essential for tailored suggestions in real time. Through the integration of mobile agents with GIS capabilities, our suggested approach directly tackles these issues. This connection enhances the system's ability to integrate numerous data sources effectively in addition to enabling dynamic adaptations to real-time user contexts and locations. Our methodology overcomes the drawbacks of conventional approaches by integrating GISs and mobile agents to provide a more reliable, scalable, and responsive solution for e-commerce customization. This guarantees that our recommendation engine will be able to provide recommendations that are more precise, fast, and context-aware, marking a major breakthrough in the area of e-commerce customization.