基于加权派系的个性化信息推荐研究
|
摘要
【目的/意义】面对网络时代数据的海量性和无序性,为用户推荐个性化资源有利于增强用户间合作、提高知识的共享速度,对新知识的发现具有深远意义。【方法/过程】基于具有相同兴趣用户的聚合优于单纯的信息聚合,构建基于社会化标注系统的个性化推荐模型。通过引入社会网络中用户使用标签的频次来选择与用户关联显著的标签,并通过加权派系发现和聚合"小众"凝聚组群和相似标签集,进而为用户推荐优质资源,使其真正契合用户的个性化需求偏好。【结果/结论】结果表明模型能够有效实现信息的个性化推荐,消除单独聚类带来的粗糙数据集,并通过抓取豆瓣上的数据进行实证分析。
【Purpose/significance】In the face of the massive and disorder of the data in the Internet era,it is helpful to enhance the cooperation between users and improve the sharing speed of knowledge, which is of great significance to the discovery of new knowledge.【Method/process】Based on the aggregation of the users with the same interest is better than the simple information aggregation, this paper constructed a personalized information model which is based on the folksonomy system.This paper introduced label frequency with the user on social networks to select a significant label associated with the user, and through the weighted cliques to find and aggregate "niche" cohesive group and similar label set, and then recommended for the high-quality resources for the user, so that it really fit the user's personalized needs preferences.【Result/conclusion】The results show that the model can effectively realize the information personalized recommendation,and reduce the rough dataset bring by separate clustering,and the empirical analysis is conducted by data grabbing from Douban.
【Purpose/significance】In the face of the massive and disorder of the data in the Internet era,it is helpful to enhance the cooperation between users and improve the sharing speed of knowledge, which is of great significance to the discovery of new knowledge.【Method/process】Based on the aggregation of the users with the same interest is better than the simple information aggregation, this paper constructed a personalized information model which is based on the folksonomy system.This paper introduced label frequency with the user on social networks to select a significant label associated with the user, and through the weighted cliques to find and aggregate "niche" cohesive group and similar label set, and then recommended for the high-quality resources for the user, so that it really fit the user's personalized needs preferences.【Result/conclusion】The results show that the model can effectively realize the information personalized recommendation,and reduce the rough dataset bring by separate clustering,and the empirical analysis is conducted by data grabbing from Douban.
引文
1熊回香.面向Web3.0的大众分类研究[D].武汉:华中师范大学,2011.
2 Zhou Tao,Ren Jie,Medo M,et al.Bipartite network projection and personal recommendation[EB/OL].https://wenku.baidu.com/view/c711f80502020740b e1e9b3d.html,2012-08-22.
3 BENGHOZI P J,et al.The long tail:myth or reality[J].International Journal of Arts Management,2010,12(3):43-53.
4 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.
5 尼尔森.Nielsen:消费者对广告的信任及购买的意愿[EB/OL].http://www.199it.com/archives/156477.ht ml,2017-03-25.
6 胡吉明,张蔓蒂.基于用户—资源关联的社会化小众推荐模型研究[J].情报理论与实践,2014,(4):123-126.
7 Sarwar B,Karypis G,Konstan J,et al.Analysis of recommendation algorithms fore-commerce[C]//Proceedings of the 2nd ACM Conference on Electronic commerce.New York:ACM Press,2000:158-167.
8 AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold starting problem[J].Information Science,2008,178(1):37-51.
9 杨丽娜,刘科成,颜志军.虚拟研究社区中的知识分享与个性化知识推荐[J].中国电化教育,2010,(6):108-112.
10 金建国.聚类方法综述[J].计算机科学,2014,(S2):288-293.
11 Luce R,Perry A.A method of matrix analysis of group Structure[J].Psychometrika,1949,14(2):95-116.
12 Scott J.Social network analysis:a handbook[M].Liu Jun,trans.Chongqing:Chongqing University Press,2007:95-100.
13 滕广青.Folksonomy模式中紧密型领域知识群落动态演化研究[J].中国图书馆学报,2016,(4):51-63.
14 熊回香,王学东.社会化标注系统中基于关联规则的Tag资源聚类研究[J].情报科学,2013,(9):73-77,98.
15 武慧娟,Jia Tina Du,孙鸿飞,Jannatul Fardous.基于K-核塌缩序列的社会化资源推荐中核心用户发现研究[J].现代图书情报技术,2016,(9):58-64.
16 李彬,汪天飞,刘才铭,等.基于相对Hamming距离的Web聚类算法[J].计算机应用,2011,31(5):1387-1390.
17[EB/OL].https://book.douban.com/,2017-03-14.
18 白如江,冷伏海.k-clique社区知识创新演化方法研究[J].图书情报工作,2013,(17):86-94.
2 Zhou Tao,Ren Jie,Medo M,et al.Bipartite network projection and personal recommendation[EB/OL].https://wenku.baidu.com/view/c711f80502020740b e1e9b3d.html,2012-08-22.
3 BENGHOZI P J,et al.The long tail:myth or reality[J].International Journal of Arts Management,2010,12(3):43-53.
4 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.
5 尼尔森.Nielsen:消费者对广告的信任及购买的意愿[EB/OL].http://www.199it.com/archives/156477.ht ml,2017-03-25.
6 胡吉明,张蔓蒂.基于用户—资源关联的社会化小众推荐模型研究[J].情报理论与实践,2014,(4):123-126.
7 Sarwar B,Karypis G,Konstan J,et al.Analysis of recommendation algorithms fore-commerce[C]//Proceedings of the 2nd ACM Conference on Electronic commerce.New York:ACM Press,2000:158-167.
8 AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold starting problem[J].Information Science,2008,178(1):37-51.
9 杨丽娜,刘科成,颜志军.虚拟研究社区中的知识分享与个性化知识推荐[J].中国电化教育,2010,(6):108-112.
10 金建国.聚类方法综述[J].计算机科学,2014,(S2):288-293.
11 Luce R,Perry A.A method of matrix analysis of group Structure[J].Psychometrika,1949,14(2):95-116.
12 Scott J.Social network analysis:a handbook[M].Liu Jun,trans.Chongqing:Chongqing University Press,2007:95-100.
13 滕广青.Folksonomy模式中紧密型领域知识群落动态演化研究[J].中国图书馆学报,2016,(4):51-63.
14 熊回香,王学东.社会化标注系统中基于关联规则的Tag资源聚类研究[J].情报科学,2013,(9):73-77,98.
15 武慧娟,Jia Tina Du,孙鸿飞,Jannatul Fardous.基于K-核塌缩序列的社会化资源推荐中核心用户发现研究[J].现代图书情报技术,2016,(9):58-64.
16 李彬,汪天飞,刘才铭,等.基于相对Hamming距离的Web聚类算法[J].计算机应用,2011,31(5):1387-1390.
17[EB/OL].https://book.douban.com/,2017-03-14.
18 白如江,冷伏海.k-clique社区知识创新演化方法研究[J].图书情报工作,2013,(17):86-94.