CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
ACM Transactions on Internet Technology (TOIT)
Automatic query expansion based on divergence
Proceedings of the tenth international conference on Information and knowledge management
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
The Journal of Machine Learning Research
Query expansion using associated queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Hybrid Pre-Query Term Expansion using Latent Semantic Analysis
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Information Systems
Information Systems
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In this paper, we propose a novel approach to expand queries by exploring both location information and topic information of the queries. Users at different locations tend to have different vocabularies, while the different expressions coming from different vocabularies may relate to the same topics. Thus these expressions are identified as location sensitive and can be used for query expansion. We propose a hierarchical query expansion model, which employs a two-level SVM classification model to classify queries as location sensitive or location non-sensitive, where the former are further classified into same location sensitive and different location sensitive. For the location sensitive queries, we propose an LDA based topic-level query similarity measure to rank the list of similar queries. Experiments with 2G raw log data from CiteSeer and Excite show that our hierarchical classification model predicts the query location sensitivity with more than 80% precision and that the final search result is significantly better than existing query expansion methods.