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We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter.
The equivalence of two extremum problems
Kiefer, J. and Wolfowitz, J · 1960
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Associative reinforcement learning using linear probabilistic concepts
Abe, N. and Long, P. M · 1999
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Minimizing regret: The general case
Rustichini, A · 1999
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Using confidence bounds for exploitation-exploration trade-offs
Auer, P · 2002
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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Learning diverse rankings with multi-armed bandits
Radlinski, F., Kleinberg, R., and Joachims, T · 2008
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Parametric bandits: The generalized linear case
Filippi, S., Cappe, O., Garivier, A., and Szepesvári, C · 2010
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A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., and Schapire, R. E · 2010
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Improved algorithms for linear stochastic bandits
Abbasi-Yadkori, Y., Pál, D., and Szepesvári, C · 2011
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A probabilistic method for inferring preferences from clicks
Hofmann, K., Whiteson, S., and De Rijke, M · 2011
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Online-to-confidence-set conversions and application to sparse stochastic bandits
Abbasi-Yadkori, Y., Pal, D., and Szepesvári, C · 2012
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Towards minimax policies for online linear optimization with bandit feedback
Bubeck, S., Cesa-Bianchi, N., and Kakade, S · 2012
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Ranked bandits in metric spaces: learning diverse rankings over large document collections
Slivkins, A., Radlinski, F., and Gollapudi, S · 2013
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Best-arm identification in linear bandits
Soare, M., Lazaric, A., and Munos, R · 2014
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Spectral bandits for smooth graph functions
Valko, M., Munos, R., Kveton, B., and Kocák, T · 2014
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Online learning to rank: Absolute vs. relative
Chen, Y. and Hofmann, K · 2015
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Click Models for Web Search
Chuklin, A., Markov, I., and de Rijke, M · 2015
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Learning to rank: Regret lower bounds and efficient algorithms
Combes, R., Magureanu, S., Proutiere, A., and Laroche, C · 2015
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Contextual combinatorial cascading bandits
Li, S., Wang, B., Zhang, S., and Chen, W · 2016
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Minimum-volume ellipsoids: Theory and algorithms
Todd, M. J · 2016
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Cascading bandits for large-scale recommendation problems
Zong, S., Ni, H., Sung, K., Ke, R. N., Wen, Z., and Kveton, B · 2016
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Scalable generalized linear bandits: Online computation and hashing
Jun, K., Bhargava, A., Nowak, R., and Willett, R · 2017
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Fully adaptive algorithm for pure exploration in linear bandits
Xu, L., Honda, J., and Sugiyama, M · 2017
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Online learning to rank in stochastic click models
Zoghi, M., Tunys, T., Ghavamzadeh, M., Kveton, B., Szepesvári, C., and Wen, Z · 2017
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Cascading bandits: Learning to rank in the cascade model
Kveton, B., Szepesvári, C., Wen, Z., and Ashkan, A · 2015
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Learning to Rank: Online Learning, Statistical Theory and Applications
Chaudhuri, S · 2016
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The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2016
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Volumetric spanners: an efficient exploration basis for learning
Hazan, E. and Karnin, Z · 2016
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DCM bandits: Learning to rank with multiple clicks
Katariya, S., Kveton, B., Szepesvári, C., and Wen, Z · 2016
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Multiple-play bandits in the position-based model
Lagree, P., Vernade, C., and Cappé, O · 2016
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Bandit Algorithms
Lattimore, T. and Szepesvári, C · 2018
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Toprank: A practical algorithm for online stochastic ranking
Lattimore, T., Kveton, B., Li, S., and Szepesvári, C · 2018
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Online clustering of contextual cascading bandits
Li, S. and Zhang, S · 2018
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Contextual dependent click bandit algorithm for web recommendation
Liu, W., Li, S., and Zhang, S · 2018
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Position bias estimation for unbiased learning to rank in personal search
Wang, X., Golbandi, N., Bendersky, M., Metzler, D., and Najork, M · 2018
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