Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Click data as implicit relevance feedback in web search
Information Processing and Management: an International Journal
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
Comparative analysis of clicks and judgments for IR evaluation
Proceedings of the 2009 workshop on Web Search Click Data
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
Global ranking by exploiting user clicks
Proceedings of the 32nd international ACM SIGIR conference on Research and development 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
Using clicks as implicit judgments: expectations versus observations
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Comparing the sensitivity of information retrieval metrics
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
A probabilistic method for inferring preferences from clicks
Proceedings of the 20th ACM international conference on Information and knowledge management
Large-scale validation and analysis of interleaved search evaluation
ACM Transactions on Information Systems (TOIS)
The K-armed dueling bandits problem
Journal of Computer and System Sciences
Thompson sampling: an asymptotically optimal finite-time analysis
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
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
Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods
ACM Transactions on Information Systems (TOIS)
| Hi-index | 0.00 |
A key challenge in information retrieval is that of on-line ranker evaluation: determining which one of a finite set of rankers performs the best in expectation on the basis of user clicks on presented document lists. When the presented lists are constructed using interleaved comparison methods, which interleave lists proposed by two different candidate rankers, then the problem of minimizing the total regret accumulated while evaluating the rankers can be formalized as a K-armed dueling bandits problem. In this paper, we propose a new method called relative confidence sampling (RCS) that aims to reduce cumulative regret by being less conservative than existing methods in eliminating rankers from contention. In addition, we present an empirical comparison between RCS and two state-of-the-art methods, relative upper confidence bound and SAVAGE. The results demonstrate that RCS can substantially outperform these alternatives on several large learning to rank datasets.