Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
ACM Transactions on Information Systems (TOIS)
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Geographic intention and modification in web search
International Journal of Geographical Information Science
Intentional query suggestion: making user goals more explicit during search
Proceedings of the 2009 workshop on Web Search Click Data
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Discovering users' specific geo intention in web search
Proceedings of the 18th international conference on World wide web
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Hermes: Data Web search on a pay-as-you-go integration infrastructure
Web Semantics: Science, Services and Agents on the World Wide Web
Intent-Based Categorization of Search Results Using Questions from Web Q&A Corpus
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Inferring query intent from reformulations and clicks
Proceedings of the 19th international conference on World wide web
Personalize web search results with user's location
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Mining interests for user profiling in electronic conversations
Expert Systems with Applications: An International Journal
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Personalized web search according to user's geographic and temporal preferences can improve search results quality and satisfy user's different information needs. We propose a novel approach to capture user's geographic and temporal preferences in the form of query profile and user preference profile by mining search results and user click-through data leveraging knowledge bases. Our approach classifies queries into five classes based on decision tree algorithm. When personalizing search results, different weights are set to different query classes to balance among content, geographic and temporal information associated with a query. The experiment evaluation results show the effectiveness of our approach and improvement of the search quality.