[1]邓 泓,吴 祎,于程远*,等.基于可信预测值的协同过滤推荐算法[J].江西师范大学学报(自然科学版),2022,(06):642-648.[doi:10.16357/j.cnki.issn1000-5862.2022.06.12]
　DENG Hong,WU Yi,YU Chengyuan*,et al.The Collaborayive Filtering Recommendation Algorithm Based on Reliable Prediction Value[J].Journal of Jiangxi Normal University:Natural Science Edition,2022,(06):642-648.[doi:10.16357/j.cnki.issn1000-5862.2022.06.12]
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基于可信预测值的协同过滤推荐算法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2022年06期
页码:
642-648
栏目:
信息科学与技术
出版日期:
2022-11-25

文章信息/Info

Title:
The Collaborayive Filtering Recommendation Algorithm Based on Reliable Prediction Value
文章编号:
1000-5862(2022)06-0642-07
作者:
邓 泓1; 吴 祎2; 于程远2*; 袁徽鹏2
1.江西农业大学软件学院,江西 南昌 330045; 2.江西农业大学计算机与信息工程学院,江西 南昌 330049)
Author(s):
DENG Hong1; WU Yi2; YU Chengyuan2*; YUAN Huipeng2
1.Software College,Jiangxi Agricultural University,Nanchang Jiangxi 330045,China; 2.School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang Jiangxi 330049,China)
关键词:
协同过滤;  推荐精度;  可信度;  可信预测值;  鲁棒性
Keywords:
collaborative filtering;  recommendation accuracy;  credibility;  credible predicted value;  robustness
分类号:
TP 311
DOI:
10.16357/j.cnki.issn1000-5862.2022.06.12
文献标志码:
A
摘要:
针对在传统协同过滤算法中存在的推荐精度较低、预测质量不佳的问题,该文提出一种基于可信预测值的协同过滤算法(RPCF).该算法在使用基于记忆的协同过滤方法计算预测值的基础上,引入可信度概念和技术方法,运用对推荐项目评级的邻居数评估可信度,融合可信度与传统预测值得到可信预测值,再根据可信预测值进行推荐,从而达到提升算法质量的目标.在MovieLens数据集中与其他提高精度方法进行实验对比,实验结果表明:RPCF方法能够提高预测精度和算法鲁棒性,具有更好的推荐质量.
Abstract:
To address the problem of low recommendation accuracy and poor prediction quality in traditional collaborative filtering algorithms,the collaborative filtering algorithm based on reliable prediction value(RPCF)is proposed.On the basis of the predicted value calculated by memory-based collaborative filtering method,the algorithm introduces the concept and technical method of credibility,and employs the number of rated neighbors to evaluate credibility,the credibility and traditional predicted value is fused to obtain the credible predicted value,then the recommendation is generated according to credible prediction value,so as to achieve the goal of improving the quality of the algorithm.Compared with other methods to improve accuracy on the MovieLens dataset,experimental results show that RPCF can improve prediction accuracy and algorithm robustness,and has better recommendation quality.

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备注/Memo

备注/Memo:
收稿日期:2022-03-16
基金项目:国家自然科学基金(62262028)和江西省教育厅科技课题(GJJ170268,GJJ200438,GJJ210438)资助项目.
作者简介:邓 泓(1977—),男,江西都昌人,副教授,主要从事农业信息化与图像处理的研究.E-mail:jxaudh@aliyun.com
通信作者:于程远(1982—),男,江西南昌人,讲师,博士,主要从事数据挖掘和信息推荐的研究.E-mail:yucy@jxau.edu.cn
更新日期/Last Update: 2022-11-25