期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：8-4
出版社：Seventh Sense Research Group
摘要：In the market, various domainspecific search engines emerged, which are restricted to specific topicalities or document formats, and vertical to the broadbased search. Simply applying the ranking model trained for the broadbased search to the verticals cannot achieve a sound performance due to the domain differences, while building different ranking models for each domain is both laborious for labelling sufficient training samples and timeconsuming or the training process. In this paper, to address the above difficulties, we investigate two problems (1) whether we can adapt the ranking model learned for existing Web page search or verticals, to the new domain, so that the amount of labelled data and the training cost is reduced, while the performance requirement is still satisfied; and (2) how to adapt the ranking model from auxiliary domains to a new target domain. We address the second problem from the regularization framework and an algorithm called ranking adaptation SVM is proposed. The results demonstrate the applicability’s of the proposed ranking model adaptation algorithm and the ranking adaptability measurement.
