Implementation and evaluation of a quality-based search engine

In this paper, an approach for the implementation of a quality-based Web search engine is proposed. Quality retrieval is introduced and an overview on previous efforts to implement such a service is given. Machine learning approaches are identified as the most promising methods to determine the quality of Web pages. Features for the most appropriate characterization of Web pages are determined. A quality model is developed based on human judgments. This model is integrated into a meta search engine which assesses the quality of all results at run time. The evaluation results show that quality based ranking does lead to better results concerning the perceived quality of Web pages presented in the result set. The quality models are exploited to identify potentially important features and characteristics for the quality of Web pages.