Abstract
One of the web search engines’ challenges is to identify the quality of web pages independent of a given user request. Web high-quality pages provide readers proper entries to get more concentrated required information on the web. This paper focuses on topic-independent web high-quality page selection to reduce web information redundancies and clean noise. Different non-content features and their effects on high-quality page selection are studied. Then K-means clustering with these features is performed to separate high-quality pages from common ones. Experiments on 19GB (document size) TREC web data set (.GOV data) have been made. By this proposed approach, less than 50% of web pages are obtained as high-quality ones, covering about 90% key information in the whole set. Information retrieval on this high-quality page set achieves more than 40% improvement, compared with that on the whole data collection.
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Wang, C., Liu, Y., Zhang, M., Ma, S. (2005). Topic-Independent Web High-Quality Page Selection Based on K-Means Clustering. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_43
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DOI: https://doi.org/10.1007/11562382_43
Publisher Name: Springer, Berlin, Heidelberg
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