A Ranking Model Framework for Multiple Vertical Search Domains
Ramesh Gadde, Shoban Babu Sriramoju · Zenodo (CERN European Organization for Nuclear Research) · 2014
Huge information can be obtained from vertical search domains. Often the information is very large in such a way that users need to browse further to get the required piece of information. In this context ranking plays an important role. When ranking is required for every domain, it is tedious task to develop ranking algorithms for every domain explicitly. Therefore it is the need of the hour to have a ranking model that can adapt to various domains implicitly. Recently Geng et al. proposed an algorithm that has a ranking model which can adapt to various domains. This avoids the process of writing various algorithms for different domains. In this paper we built a prototype application that implements the algorithm to provide ranking to the search results of various domains. The experimental results reveal that the algorithm is effective and can be used in the real world applications.