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

@inproceedings{citeulike:4387877, abstract = {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.}, address = {New York, NY, USA}, author = {Cao, Huanhuan and Jiang, Daxin and Pei, Jian and Chen, Enhong and Li, Hang}, booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web}, citeulike-article-id = {4387877}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1526709.1526736}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1526709.1526736}, comment = {[Talk] Take session information into account when preparing a recommendation. Variable-length HMM. Each hidden state corresponds to users' search intents. Use previous work by Cao et al. to help in the parameter estimate. Map-reduce implementation of the learning part. Applications: (1) re-rank based on context queries (2) query recommendation. }, doi = {10.1145/1526709.1526736}, isbn = {978-1-60558-487-4}, keywords = {clicks, context, queries, ranking}, location = {Madrid, Spain}, pages = {191--200}, posted-at = {2009-04-23 16:26:27}, priority = {4}, publisher = {ACM}, title = {Towards context-aware search by learning a very large variable length hidden markov model from search logs}, url = {http://dx.doi.org/10.1145/1526709.1526736}, year = {2009} }

TY - CONF ID - citeulike:4387877 L3 - citeulike-article-id:4387877 N2 - 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. CY - Madrid, Spain SN - 978-1-60558-487-4 AD - New York, NY, USA EP - 200 TI - Towards context-aware search by learning a very large variable length hidden markov model from search logs PB - ACM T2 - WWW '09: Proceedings of the 18th international conference on World wide web SP - 191 KW - clicks KW - context KW - queries KW - ranking N1 - [Talk] Take session information into account when preparing a recommendation. Variable-length HMM. Each hidden state corresponds to users' search intents. Use previous work by Cao et al. to help in the parameter estimate. Map-reduce implementation of the learning part. Applications: (1) re-rank based on context queries (2) query recommendation. AU - Cao, Huanhuan AU - Jiang, Daxin AU - Pei, Jian AU - Chen, Enhong AU - Li, Hang PY - 2009/// UR - http://dx.doi.org/10.1145/1526709.1526736 ER -