Ranking categories for web search

• Published in: Proceedings of the IR research, 30th European conference on Advances in information retrieval pp. 564 - 569
• Main Authors: Demartini, Gianluca, Chirita, Paul-Alexandru, Brunkhorst, Ingo, Nejdl, Wolfgang
• Format: Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer-Verlag 30.03.2008
• Series: ACM Other Conferences
• Subjects: Information systems; Information systems > Information retrieval; Information systems > Information retrieval > Information retrieval query processing; Information systems > Information retrieval > Retrieval tasks and goals; Information systems > Information retrieval > Retrieval tasks and goals > Clustering and classification; Information systems > Information systems applications; Information systems > Information systems applications > Data mining; Information systems > Information systems applications > Data mining > Clustering
• ISBN: 9783540786450; 3540786457
• DOI: 10.5555/1793274.1793344

In the context of Web Search, clustering based engines are emerging as an alternative for the classical ones. In this paper we analyse different possible ranking algorithms for ordering clusters of documents within a search result. More specifically, we investigate approaches based on document rankings, on the similarities between the user query and the search results, on the quality of the produced clusters, as well as some document independent approaches. Even though we use a topic based hierarchy for categorizing the URLs, our metrics can be applied to other clusters as well. An empirical analysis with a group of 20 subjects showed that the average similarity between the user query and the documents within each category yields the best cluster ranking.