An efficient hybrid recommendation model based on collaborative filtering recommender systems

作     者：Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah 

作者机构：Department of Computer ScienceMangalore UniversityMangaloreIndia 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2021年第6卷第4期

页      面：480-492页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0839[工学-网络空间安全] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：services filtering similarity 

摘      要：In recent years,collaborative filtering(CF)techniques have become one of the most popularly used techniques for providing personalised services to users.CF techniques collect users9 previous information about items such as books,music,movies,ideas,and so on.Memory-based models are generally referred to as similarity-based CF models,which are one of the most widely agreeable approaches for providing service recommendations.The memory-based approach includes user-based CF(UCF)and item-based CF(ICF)algorithms.The UCF model recommends items by finding similar users,while the ICF model recommends items by finding similar items based on the user-item rating matrix.However,consequent to the ingrained sparsity of the user-item rating matrix,a large number of ratings are missing.This results in the availability of only a few ratings to make predictions for the unknown ratings.The result is the poor prediction quality of the CF model.A model to find the best algorithm is provided here,which gives the most accurate recommendation based on different similarity metrics.Here a hybrid recommendation model,namely rUICF,is proposed.The rUICF model integrates the UCF and ICF models with the T linear regression model to model the sparsity and scalability issue of the user-item rating matrix.Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.