Making large-scale support vector machine learning practical
Advances in kernel methods
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Tailoring click models to user goals
Proceedings of the 2009 workshop on Web Search Click Data
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Evaluation of methods for relative comparison of retrieval systems based on clickthroughs
Proceedings of the 18th ACM conference on Information and knowledge management
A probabilistic method for inferring preferences from clicks
Proceedings of the 20th ACM international conference on Information and knowledge management
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Estimating interleaved comparison outcomes from historical click data
Proceedings of the 21st ACM international conference on Information and knowledge management
Reusing historical interaction data for faster online learning to rank for IR
Proceedings of the sixth ACM international conference on Web search and data mining
Optimized interleaving for online retrieval evaluation
Proceedings of the sixth ACM international conference on Web search and data mining
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for IR. Lerot includes several online learning algorithms, interleaving methods and a full suite of ways to evaluate these methods. In the absence of real users, the evaluation method bundled in the software package is based on simulations of users interacting with the search engine. The software presented here has been used to verify findings of over six papers at major information retrieval venues over the last few years.