exploiting user feedback to improve quality of search results clustering
Search result clustering provides an intuitive overview toward information contained in the search result. The goal of this research is to implement a clustering engine to provide search result clustering for various search tasks retrieving items, or objects whose contents do not contain descriptive text. Content-based similarity measures used for traditional clustering engines are not suitable for general measure, because of its domain-specific nature and lack of descriptiveness. To remedy the problems, we exploit user feedback information to measure similarity between items. As the first approach to use user feedback information to measure similarity between general items to cluster them, we explore similarity models and algorithms suitable for clustering. To realize usefulness of the presented clustering method, performance of the clustering is evaluated using some real-world data sets. The presented method produces more accurate clusters than clustering methods based on traditional content-based measures do. After optimizing the method, a web-based application based on the clustering method is implemented. The flexibility of the implemented system and the application enables search results clustering to be applied to search results containing various type of objects.
user feedback search results clustering interactive search