Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
A vector space model for automatic indexing
Communications of the ACM
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster Ranking with an Application to Mining Mailbox Networks
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A new suffix tree similarity measure for document clustering
Proceedings of the 16th international conference on World Wide Web
Supporting ranking and clustering as generalized order-by and group-by
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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The current commercial Web search engines do a good job at ranking web pages with hyperlink information. However, there are also many common documents such as PowerPoint files or Flash files which do not have enough hyperlink information. We call such documents weak-linked documents. Current search engines return therefore either completely irrelevant results or poorly ranked documents when searching for these files. This paper addresses this problem and proposes a solution: RoC (Ranking weak-linked documents based on Clustering). For a given query q, RoC 1) first clusters traditional Web page search results in order to find what topics existing on the WWW are interesting to the users, 2) then assigns a weight to each topic cluster based on the ranks of the web pages in it, and finally 3) ranks all relevant weak-linked documents based on their similarity to the weighted clusters obtained from the Web. The experiments show that our approach considerably improves the result quality of current search engines and that of latent semantic indexing.