An Interactive Perception Method Based Collaborative Rating Prediction Algorithm  

作　　者：YAN Wenjie ZHANG Jiahao LI Ziqi 

机构地区：[1]School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China

出　　处：《Chinese Journal of Electronics》2023年第1期97-110,共14页电子学报（英文版）

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

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).

关 键 词：Recommender systems Probabilistic matrix factorization Convolutional autoencoder Interactive perception Multi-angle optimization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]