Value-aware Recommendation based on Reinforcement Profit Maximization
Changhua Pei, Xinru Yang, Qing Chun Cui, Xiao Lin, Fei Long Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang · 2019
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the final revenue/profit of the system. Traditional recommendation targets such as rating prediction and top-k recommendation are not directly related to this goal.