Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
IEEE Internet Computing
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Analysis of geographic queries in a search engine log
Proceedings of the first international workshop on Location and the web
Exploring mouse movements for inferring query intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
(Web Search)shared: Social Aspects of a Collaborative, Community-Based Search Network
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Tag data and personalized information retrieval
Proceedings of the 2008 ACM workshop on Search in social media
Proceedings of the 1st International Workshop on Context-Aware Middleware and Services: affiliated with the 4th International Conference on Communication System Software and Middleware (COMSWARE 2009)
Mining rich session context to improve web search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Information Foraging Theory as a Form of Collective Intelligence for Social Search
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Mining search engine clickthrough log for matching N-gram features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Factors affecting click-through behavior in aggregated search interfaces
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An analysis of user behavior in online video streaming
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Action prediction and identification from mining temporal user behaviors
Proceedings of the fourth ACM international conference on Web search and data mining
Bridging topic modeling and personalized search
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Transactions on computational collective intelligence II
Rediscovering missing web pages using link neighborhood lexical signatures
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Find, new, copy, web, page - tagging for the (re-)discovery of web pages
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
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The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-of-the-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques.