[1]滕少华,麦嘉俊,张 巍,等.一种基于混合相似度的用户多兴趣推荐算法[J].江西师范大学学报(自然科学版),2016,40(05):481-486.
　TENG Shaohua,MAI Jiajun,ZHANG Wei,et al.User Multi-Faced Interests Recommendation Algorithm Based on Hybrid Similarity[J].Journal of Jiangxi Normal University:Natural Science Edition,2016,40(05):481-486.
点击复制

一种基于混合相似度的用户多兴趣推荐算法()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2016年05期
页码:
481-486
栏目:
出版日期:
2016-10-01

文章信息/Info

Title:
User Multi-Faced Interests Recommendation Algorithm Based on Hybrid Similarity
作者:
滕少华; 麦嘉俊; 张 巍; 赵淦森
1.广东工业大学计算机学院,广东 广州 510006; 2.华南师范大学计算机学院,广东 广州 510631
Author(s):
TENG Shaohua; MAI Jiajun; ZHANG Wei; ZHAO Gansen
1.School of Computer Science and Technology,Guangdong University of Technology,Guangdong Guangzhou 510006,China; 2.School of Computer,South China Normal University,Guangdong Guangzhou 510631,China
关键词:
用户多兴趣;  推荐算法;  协同过滤;  混合相似度
Keywords:
user multi-faced interests;  recommendation algorithm;  collaborative filtering;  hybrid similarity computing
分类号:
TP 311
摘要:
针对传统协同过滤推荐数据稀疏会影响推荐质量,以及项目最近邻居集的计算忽略用户多兴趣及提高推荐的准确度问题,该文采用混合模型改进了相似性度量计算,综合Pearson相关系数与修正余弦相似性,提出了一种基于混合相似度的用户多兴趣推荐算法.实验表明:该推荐方法的相似度计算更高效,不仅提高推荐准确率,而且使用户有更好的推荐体验.
Abstract:
The traditional collaborative filtering recommendation’s sparse data will affect the quality,and it fails to take into account the user multi-faced interests to determine the projects nearest neighbor set.Coupling with the traditional similarity measure method without considering user’s behavior,leads to lower quality of the recommendation.In order to improve the recommendation accuracy,the hybrid model,improved similarity measure calculated by Pearson correlation linear combination of adjusted cosine correlation has been used,and then an user multi-faced interests recommendation algorithm of hybrid similarity computing is proposed in the paper.The experimental results show that the similarity calculation of recommend dation method is more efficient,improve the accuracy of recommendation,and make the better recommendation of user experience.

参考文献/References:

[1] Sarwar B,Karypis G,Konstan,et al.Item-based collaborative filtering recommendation algorithms [C].New York:ACM,2001:285-295.
[2] 刘建国,周涛,汪秉宏.个性化推荐系统的研究进展 [J].自然科学进展,2009,19(1):1-15.
[3] 黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法 [J].计算机学报,2010,33(8):1369-1377.
[4] Liu Qi,Chen Enhong,Xiong Hui,et al.Enhancing collaborative filtering by user interests expansion via personalized ranking [J].IEEE Trans on Systems,Man and Cybernetics-B,2012,42(1):218-233.
[5] 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法 [J].软件学报,2003,4(9):1621-1628.
[6] Zhao Zhidan,Shang Mingsheng.User-based collaborative-filtering recommendation algorithms on hadoop [EB/OL].
[2015-12-17].https://www.researchgate.net/publication/221306166_User-Based_Collaborative-Filtering_Recommendation_Algorithms_on_Hadoop.
[7] 孙光福,吴乐,刘淇,等.基于时序行为的协同过滤推荐算法 [J].软件学报,2013,24(11):2721-2733.
[8] 刘冬宁,刘艳,滕少华,等.基于角色协同的在线社交网络好友推荐机制 [J].广西大学学报:自然科学版,2014,39(6):1316-1323.
[9] 马小军,赵伟.改进相似度的分布式个性化推荐 [J].计算机工程与应用,2014,50(4):126-131.
[10] 李国,张智斌,刘芳先,等.非线性组合的协同过滤推荐算法 [J].计算机应用,2011(11):3063-3067.
[11] 高灵渲,张巍,霍颖翔,等.改进的聚类模式过滤推荐算法 [J].江西师范大学学报:自然科学版,2012,36(1):106-110.
[12] 庄永龙.基于项目特征模型的协同过滤推荐算法 [J].计算机应用与软件,2009,26(5):244-246.
[13] 吴月萍,郑建国.协同过滤推荐算法 [J].计算机工程与设计,2011,32(9):3019-3021.
[14] Teng Shaohua,Li Junlei,Li Rigui,et al.The calculation of similarity and its application in data mining [J].Lecture Notes in Computer Science,2014,8351:563-574.
[15] 许智宏,王宝莹.基于项目综合相似度的协同过滤算法 [J].计算机应用研究,2014,31(2):398-400.

备注/Memo

备注/Memo:
收稿日期:2016-01-12基金项目:国家自然科学基金(61402118),广东省科技计划项目(2012B091000173,2013B010401034,2013B090200017,2013B010401029),广东省教育厅项目(ZYGX008),广东省重点实验室开放基金(15zk0132)和广州市科技计划(2012J5100054,2013J4500028,2013J4100004,201508010067)资助项目.作者简介:滕少华(1962-),男,江西南昌人,教授,博士,主要从事协同计
更新日期/Last Update: 1900-01-01