document: 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 on a real data set consisting of 1.8 billion queries , 2.6 billion clicks , and 840 million search sessions , and evaluate the effectiveness of the vlHMM learned from the real data on three search applications : document re-ranking , query suggestion , and URL recommendation . The experimental results show that our approach is both effective and efficient .
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