Abstract. The information on the World Wide Web is growing without bound. Users may have very diversified preferences in the pages they target through a search engine. It is therefore a challenging task to adapt a search engine to suit the needs of a particular community of users who share similar interests. In this paper, we propose a new algorithm, Ranking SVM in a Co-training Framework (RSCF). Essentially, the RSCF algorithm takes the clickthrough data containing the items in the search result that have been clicked on by a user as an input, and generates adaptive rankers as an output. By analyzing the clickthrough data, RSCF first categorizes the data as the labelled data set, which contains the items that have been scanned already, and the unlabelled data set, which contains the items that have not yet been scanned. The labelled data is then augmented with unlabelled data to obtain a larger data set for training the rankers. We demonstrate that the RSCF algorithm produces better ranking results than the standard Ranking SVM algorithm. Based on RSCF we develop a metasearch engine that comprises MSNSearch, Wisenut, and Overture, and carry out an online experiment to show that our metasearch engine outperforms Google.
Recently, an RSCF algorithm REF has been proposed to extend ranking SVM to a co-training framework in order to tackle the lack of clickthrough data for training.
Applying Co-training to Clickthrough Data for Search Engine Adaptation
DASFAA
Computer Science