Hierarchical Aggregation for Reputation Feedback of Services Networks

Abstract

Product ratings are popular tools to support buying decisions of consumers, which are also valuable for online retailers. In online marketplaces, vendors can use rating systems to build trust and reputation. To build trust, it is really important to evaluate the aggregate score for an item or a service. An accurate aggregation of ratings can embody the true quality of offerings, which is not only beneficial for providers in adjusting operation and sales tactics, but also helpful for consumers in discovery and purchase decisions. In this paper, we propose a hierarchical aggregation model for reputation feedback, where the state-of-the-art feature-based matrix factorization models are used. We first present our motivation. Then, we propose feature-based matrix factorization models. Finally, we address how to utilize the above modes to formulate the hierarchical aggregation model. Through a set of experiments, we can get that the aggregate score calculated by our model is greater than the corresponding value obtained by the state-of-the-art IRURe; i.e., the outputs of our models can better match the true rank orders.

Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

“…Nonlinear neural network is a new type of feedforward neural network based on nonlinear theory. Nonlinear neural network is compatible with the superiority of nonlinear and neural networks [ 17 – 20 ].…”