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
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Lerot: an online learning to rank framework
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
Relative confidence sampling for efficient on-line ranker evaluation
Proceedings of the 7th ACM international conference on Web search and data mining
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As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank, retrieval systems can learn directly from implicit feedback inferred from user interactions. In such an online setting, algorithms must obtain feedback for effective learning while simultaneously utilizing what has already been learned to produce high quality results. We formulate this challenge as an exploration---exploitation dilemma and propose two methods for addressing it. By adding mechanisms for balancing exploration and exploitation during learning, each method extends a state-of-the-art learning to rank method, one based on listwise learning and the other on pairwise learning. Using a recently developed simulation framework that allows assessment of online performance, we empirically evaluate both methods. Our results show that balancing exploration and exploitation can substantially and significantly improve the online retrieval performance of both listwise and pairwise approaches. In addition, the results demonstrate that such a balance affects the two approaches in different ways, especially when user feedback is noisy, yielding new insights relevant to making online learning to rank effective in practice.