Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

Understand

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.
• In this work, we blend the fundamental concepts in online advertising and micro-economics into personalized recommendation for profit maximization.