Online Learning to Rank with Features
This work addresses online ranking for systems handling large item sets, offering incremental improvements in efficiency and performance.
The authors tackled the problem of online learning to rank by introducing a model where click probability factors into examination and attractiveness functions, with attractiveness as a linear function of features and an unknown parameter. They developed a novel algorithm that reduces dependence on the number of items to dependence on dimension, improving regret in the orthogonal case compared to state-of-the-art.
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.