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"SeaRank: relevance prediction based on click models in a reinforcement" by Amir Hosein Keyhanipour and Farhad Oroumchian

Purpose: User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User...
User Satisfaction Click Models Earning To Rank Q Learning Random Forest Reinforcement Learning
Source: uow.edu.au

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