An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

作     者：YAN Wenjie ZHANG Jiahao LI Ziqi YAN Wenjie;ZHANG Jiahao;LI Ziqi

作者机构：School of Artificial Intelligence Hebei University of Technology 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第1期

页      面：97-110页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (61702157) 

主　　题：Recommender systems Probabilistic matrix factorization Convolutional autoencoder Interactive perception Multi-angle optimization 

摘      要：To solve the rating prediction problems of low accuracy and data sparsity on different datasets,we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF,by fusing dual convolutional autoencoder(DCAE) and probability matrix factorization(PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error(RMSE) and mean absolute error(MAE).