典型文献
Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems
文献摘要：
Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products.Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods.However,these methods have shown unpretentious performance in terms of novelty,diversity,and coverage.We propose a novel graph-based collaborative filtering method,namely Positive Multi-Layer Graph-Based Recommender System(PMLG-RS).PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations.The positive multi-layer graph consists of two connected layers:the user and item layers.PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node.A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets,MovieLens-100K,MovieLens-Last,and Film Trust.The results demonstrate the superiority of PMLG-RS and its high capability in making relevant,novel,and diverse recommendations for users.
文献关键词：
中图分类号：
作者姓名：
Bushra Alhijawi;Ghazi AL-Naymat
作者机构：
Department of Computer Science,Princess Sumaya University for Technology,Amman 11941,Jordan;Artificial Intelligence Research Center,College of Engineering and Information Technology Ajman University,Ajman 20550,U.A.E.
文献出处：
引用格式：
[1]Bushra Alhijawi;Ghazi AL-Naymat-.Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems)[J].计算机科学技术学报（英文版）,2022(04):975-990
A类：
unpretentious,PMLG
B类：
Novel,Positive,Multi,Layer,Graph,Based,Method,Collaborative,Filtering,Recommender,Systems,systems,play,increasingly,important,role,wide,variety,applications,help,users,favorite,products,filtering,has,remarkable,success,terms,accuracy,becomes,one,most,popular,methods,However,these,have,shown,performance,novelty,diversity,coverage,We,propose,graph,collaborative,namely,RS,involves,positive,multi,path,search,algorithm,generate,recommendations,consists,two,connected,layers,item,requires,developing,new,that,finds,shortest,highest,cost,from,source,node,every,other,experiments,conducted,compare,well,known,three,benchmark,datasets,MovieLens,100K,Last,Film,Trust,results,demonstrate,superiority,its,capability,making,relevant,diverse
AB值：
0.572933
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