Collaborative filtering model for user satisfaction prediction in Spoken Dialog System evaluation
Developing accurate models to automatically predict user satisfaction about the overall quality of a Spoken Dialog System (SDS) is highly desirable for SDS evaluation. In the original PARADISE framework, a linear regression model is trained using measures drawn from rated dialogs as predictors with user satisfaction as the target. In this paper, we extend PARADISE by introducing a collaborative filtering (CF) model for user satisfaction prediction and its corresponding extension. This prediction model is drawn from the idea of CF in recommendation systems, which uses information from near neighbors of an unrated dialog to predict its user satisfaction. We also present the methodology of collecting user judgments on SDS quality with crowdsourcing through Amazon Mechanical Turk. Experimental results show that the CF approaches could distinctly improve the prediction accuracy of user satisfaction.