Online Rank Aggregation


Shota Yasutake, Kohei Hatano, Eiji Takimoto, Masayuki Takeda ;
Proceedings of the Asian Conference on Machine Learning, PMLR 25:539-553, 2012.


We consider an online learning framework where the task is to predict a permutation which represents a ranking of n fixed objects. At each trial, the learner incurs a loss defined as Kendall tau distance between the predicted permutation and the true permutation given by the adversary. This setting is quite natural in many situations such as information retrieval and recommendation tasks. We prove a lower bound of the cumulative loss and hardness results. Then, we propose an algorithm for this problem and prove its relative loss bound which shows our algorithm is close to optimal.

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