A Transfer Learning Based Interpretable User Experience Model on Small Samples

Abstract

User experience (UX) is a key factor that affects software survival time. A rich line of research has studied the relationships between UX and software factors to modify software and improve user satisfaction. However, the existing machine learning models for predicting UX on small data set is not accurate enough, and research with traditional statistical methods only obtained indistinct relations among UX, user characteristics and software factors. With the goal of improving the accuracy of UX model and obtaining sufficient UX relationships, we propose Transfer in Cart (TrCart) algorithm and Transfer Adaboost in Cart (TrAdaBoostCart) algorithm. To verify this approach, we present the UX study on a desktop game and an android game. According to the experimental results, we find that the TrAdaBoostCart has better accuracy and interpretable results. Hence, the proposed approach provides important guidelines for the design process of mobile applications.