Sciweavers

Share
IJCAI
2007

Learning User Clicks in Web Search

8 years 7 months ago
Learning User Clicks in Web Search
Machine learning for predicting user clicks in Webbased 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.
Ding Zhou, Levent Bolelli, Jia Li, C. Lee Giles, H
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Ding Zhou, Levent Bolelli, Jia Li, C. Lee Giles, Hongyuan Zha
Comments (0)
books