Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

作     者：Waleed Reafee Marwa Alhazmi Naomie Salim 

作者机构：Faculty of Computer Studies and Information Technology University of Garden City11111 KhartoumSudan School of ComputingFaculty of Engineering Universiti Teknologi Malaysia81310 SkudaiJohorMalaysia 

出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)

年 卷 期：2020年第11卷第6期

页      面：129-160页

核心收录：

学科分类：080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Social recommendation explicit friendship implicit friendship correlated items quantum mechanics 

摘      要：Nowadays,with the advent of the age of Web 2.0,several social recommendation methods that use social network information have been proposed and achieved distinct develop***,the most critical challenges for the existing majority of these methods are:(1)They tend to utilize only the available social relation between users and deal just with the cold-start user issue.(2)Besides,these methods are suffering from the lack of exploitation of content information such as social tagging,which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation *** this paper,we investigated the efficiency of data fusion by integrating multi-source of ***,two essential factors,user-side information,and item-side information,are ***,we developed a novel social recommendation model called Two-Sided Regularization(TSR),which is based on the probabilistic matrix factorization ***,the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed *** results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-ofthe-art recommendation *** results indicate the importance of incorporating various sources of information in the recommendation process.