Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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The dynamic nature of document relevance is largely ignored by traditional Information Retrieval (IR) models, which assume that scores (relevance) for documents given an information need are static. In this paper, we formulate a general Dynamical Information Retrieval problem, where we consider retrieval as a stochastic, controllable process. The ranking action continuously controls the retrieval system's dynamics and an optimal ranking policy is found that maximizes the overall users' satisfaction during each period. Through deriving the posterior probability of the documents evolving relevancy from user clicks, we can provide a plug-in framework for incorporating a number of click models, which can be combined with Multi-Armed Bandit theory and Portfolio Theory of IR to create a dynamic ranking rule that takes rank bias and click dependency into account. We verify the versatility of our algorithms in a number of experiments and demonstrate improved performance over strong baselines and as a result significant performance gains have been achieved.