MaSS: Model Pricing Marketplace Based on Unit Data Contribution

Data-driven machine learning models have become ubiquitous. However, there is still very little research on how to promote the market development of the machine learning model. The existing research is mainly dividedin to two aspects, one is the interaction between the data owners and the brokers, that is, the compensation of the data owners. Another is the interaction between model buyers and brokers, that is, the expense of the model buyers. But for the model market, these issues are indivisible. Therefore, this paper takes a formal data market place perspective and proposes the novel model marketplace based on three- stage hierarchical Stack elberg game and Shapley value (MaSS). MaSS adopts a new utility evaluation index, Shapley value. And then this paper proposes a model trading framework of three- stage Stack elberg game based on Shapley value, including three- party participants: model buyers, brokers and data owners. The data owners provide the broker with private data. The broker swill further process the data into the models needed by the model buyers, and provide the model for the model buyer for profit. They interact with each other to form a Stack elberg game to maximize the profits of all involved in the transaction. And the uniqueness of the existence of equilibrium pricing strategy is proven theoretically. Finally, its remarkable performance is demonstrated by extensive simulations on real data.