Learning User Clicks in Web Search
Ding Zhou, Levent Bolelli, Jia Li, Lee Giles, Hongyuan Zha
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.