Game Perception Assessment Algorithm Based on Cumulative Link Ordered Regression
Xianxing Zhu, Yuan Jia, Gang Zhou, Fei Shi, Xiaohui Huang, Jiajia Wang · 2024
In order to solve the problem that the current online game user perception assessment method cannot accurately reflect the real perception of online game users, a new ordered regression model method based on the generalized cumulative link function is proposed. Firstly, the ordered regression model is applied to the game user perception assessment based on the naturally ordered nature of the user perception dataset; secondly, variable parameters are added on the basis of the commonly used cumulative link function in order to enhance the flexibility of the link function and adapt to different data distributions; finally, the cumulative link model is combined with the class-distance-weighted entropy loss function, which penalizes the deviation of the predicted results from the true class in order to effectively predict user-perceived data distribution. The proposed method is compared with various current ordered regression algorithms on two game user perception datasets, and the experimental results show that the proposed model is better than the comparison algorithms in terms of classification accuracy, average absolute error and quadratic weighted kappa index.