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Castillo, C.; Baeza-Yates, R.: Web retrieval and mining (2009) 0.00
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Abstract

The advent of the Web in the mid-1990s followed by its fast adoption in a relatively short time, posed significant challenges to classical information retrieval methods developed in the 1970s and the 1980s. The major challenges include that the Web is massive, dynamic, and distributed. The two main types of tasks that are carried on the Web are searching and mining. Searching is locating information given an information need, and mining is extracting information and/or knowledge from a corpus. The metrics for success when carrying these tasks on the Web include precision, recall (completeness), freshness, and efficiency.

Source

Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.00
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Abstract

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804

Kucukyilmaz, T.; Cambazoglu, B.B.; Aykanat, C.; Baeza-Yates, R.: ¬A machine learning approach for result caching in web search engines (2017) 0.00

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Source: Information processing and management. 53(2017) no.4, S.834-850

Baeza-Yates, R.; Boldi, P.; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms (2006) 0.00

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Source: http://chato.cl/papers/baeza06_general_pagerank_damping_functions_link_ranking.pdf [Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR) Conference, SIGIR'06, August 6-10, 2006, Seattle, Washington, USA]

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