Learning user clicks in web search

Ding Zhou, Levent Bolelli, Jia Li, C. Lee Giles, Hongyuan Zha

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

Machine learning for predicting user clicks in Web-based search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing a method for click prediction based on observations of past queries and the clicked documents. Due to the sparsity of the problem space, commonly encountered when learning for Web search, new approaches to learn the probabilistic relationship between documents and queries are proposed. Two probabilistic models are developed, which differ in the interpretation of the query-document co-occurrences. A novel technique, namely, conditional probability hierarchy, flexibly adjusts the level of granularity in parsing queries, and, as a result, leverages the advantages of both models.

Original languageEnglish (US)
Pages (from-to)1162-1167
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - Dec 1 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

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Learning systems
Statistical Models

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

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Learning user clicks in web search. / Zhou, Ding; Bolelli, Levent; Li, Jia; Giles, C. Lee; Zha, Hongyuan.

In: IJCAI International Joint Conference on Artificial Intelligence, 01.12.2007, p. 1162-1167.

Research output: Contribution to journalConference article

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AU - Zhou, Ding

AU - Bolelli, Levent

AU - Li, Jia

AU - Giles, C. Lee

AU - Zha, Hongyuan

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AB - Machine learning for predicting user clicks in Web-based search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing a method for click prediction based on observations of past queries and the clicked documents. Due to the sparsity of the problem space, commonly encountered when learning for Web search, new approaches to learn the probabilistic relationship between documents and queries are proposed. Two probabilistic models are developed, which differ in the interpretation of the query-document co-occurrences. A novel technique, namely, conditional probability hierarchy, flexibly adjusts the level of granularity in parsing queries, and, as a result, leverages the advantages of both models.

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