spectral geometry for simultaneously clustering and ranking query search results
How best to present query search results is an important problem in search engines and information retrieval systems. When a single query retrieves many results, simply showing them as a long list will provide users with poor overview. Nowadays, ranking and clustering query search results have been two useful separate post-processing techniques to organize retrieved documents. In this paper, we proposed a spectral analysis method based on the content similarity networks to integrate the clustering and ranking techniques for improving literature search. The new approach organizes all these search results into categories intelligently and simultaneously rank the results in each category. A variety of theoretical and empirical studies have demonstrated that the presented method performs well in real applications, especially in biomedical literature retrieval. Moreover, any free text information can be analyzed with the new method, i.e., the proposed approach can be applied to various information systems, such as Web search engines and literature search service.
spectral geometry clustering ranking query search