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

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen, Hang Li
• Keywords: Context-aware Approach, Context-aware Search, Internet Technology, Search Sessions, WWW 2009