a scalable method for online learning of non-linear preferences based on anonymous negotiation data
We consider the problem of a shop agent negotiating bilaterally with many customers about a bundle of goods or services together with a price. To facilitate the shop agent's search for mutually beneficial alternative bundles, we develop a method for online learning customers' preferences, while respecting their privacy. By introducing extra parameters, we represent customers' highly nonlinear preferences as a linear model. We develop a method for learning the underlying stochastic process of these parameters online.
online learning negotiation market-based methods