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Online Learning to Rank with Features
Shuai Li · Tor Lattimore · Csaba Szepesvari

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #128

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. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.

Author Information

Shuai Li (The Chinese University of Hong Kong)
Tor Lattimore (DeepMind)
Csaba Szepesvari (DeepMind/University of Alberta)

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