Abstract
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.
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Demartini, G., Chirita, PA., Brunkhorst, I., Nejdl, W. (2008). Ranking Categories for Web Search. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_56
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DOI: https://doi.org/10.1007/978-3-540-78646-7_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78645-0
Online ISBN: 978-3-540-78646-7
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