Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Proceedings of the fourth ACM international conference on Web search and data mining
Joint relevance and freshness learning from clickthroughs for news search
Proceedings of the 21st international conference on World Wide Web
Hierarchical composable optimization of web pages
Proceedings of the 21st international conference companion on World Wide Web
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
Improving recency ranking using twitter data
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A framework for query refinement with user feedback
Journal of Systems and Software
On modeling query refinement by capturing user intent through feedback
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
A click model for time-sensitive queries
Proceedings of the 22nd international conference on World Wide Web companion
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Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learning framework has been tremendously successful in commercial search engines, in scenarios where relevance of documents to a query changes over time, such as ranking recent documents for a breaking news query, the batch-learned ranking functions do have limitations. Users' real-time click feedback becomes a better and timely proxy for the varying relevance of documents rather than the editorial judgments provided by human editors. In this paper, we propose an online learning algorithm that can quickly learn the best re-ranking of the top portion of the original ranked list based on real-time users' click feedback. In order to devise our algorithm and evaluate it accurately, we collected exploration bucket data that removes positional biases on clicks on the documents for recency-classified queries. Our initial experimental result shows that our scheme is more capable of quickly adjusting the ranking to track the varying relevance of documents reflected in the click feedback, compared to batch-trained ranking functions.