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Towards context-aware search by learning a very large variable length hidden markov model from search logs

10 years 3 months ago
Towards context-aware search by learning a very large variable length hidden markov model from search logs
Capturing the context of a user's query from the previous queries and clicks in the same session may help understand the user's information need. A context-aware approach to document re-ranking, query suggestion, and URL recommendation may improve users' search experience substantially. In this paper, we propose a general approach to context-aware search. To capture contexts of queries, we learn a variable length Hidden Markov Model (vlHMM) from search sessions extracted from log data. Although the mathematical model is intuitive, how to learn a large vlHMM with millions of states from hundreds of millions of search sessions poses a grand challenge. We develop a strategy for parameter initialization in vlHMM learning which can greatly reduce the number of parameters to be estimated in practice. We also devise a method for distributed vlHMM learning under the map-reduce model. We test our approach
Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen,
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2009
Where WWW
Authors Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen, Hang Li
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