Abstract
Over the last decade, recommendation systems (RS) have matured into a valuable approach for assisting online customers in navigating through large product or information spaces. The associated research has described and evaluated a variety of different techniques [3] for proposing items of interest to customers. However, each of these techniques also suffers from several shortcomings. Therefore, depending on the application domain and the availability of background knowledge some algorithms and hybrid variants may be more applicable than others. However, most commercial recommendation systems are monolithic in the sense that they support only a limited subset of recommendation techniques. In this paper we therefore present ISeller, a proven industrial-strength recommendation framework for personalizing small to medium-scale e-commerce platforms. ISeller supports all basic recommendation techniques and, due to its modular architecture, hybrid variants as well. This paper focuses in particular on the generic user modeling component of ISeller as it is the prerequisite for supporting different recommendation techniques within the same application infrastructure. Furthermore, we present an application scenario showing the generic nature and wide applicability of the described user modeling component in the domain of mapbased recommendations [16].