Aggregate two-way co-clustering of ads and user data for online advertisements

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题名:	Aggregate two-way co-clustering of ads and user data for online advertisements
作者:	張嘉惠;Wu), 巫孟倫(Meng-Lun;Chang), 張嘉惠(Chia-Hui;Liu), 劉睿哲(Rui-Zhe;Fan), 范登凱(Teng-Kai
贡献者:	資訊電機學院資訊工程學系
关键词:	Advertisements;Clustering;Cobalt;Mathematical analysis;Matrices;Matrix methods;On-line systems;Online
日期:	2012-01-01
上传时间:	2026-04-23 13:13:03 (UTC+8)
出版者:	Institute of Information Science;社團法人中華民國計算語言學學會
摘要:	摘要： Clustering plays an important role in data mining, as it is used by many applications as a preprocessing step for data analysis. Traditional clustering focuses on grouping similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. In this research, we apply two-way co-clustering to the analysis of online advertising where both ads and users need to be clustered. However, in addition to the ad-user link matrix that denotes the ads which a user has linked, we also have two additional matrices, which represent extra information about users and ads. In this paper, we proposed a 3-staged clustering method that makes use of the three data matrices to enhance clustering performance. In addition, an Iterative Cross Co-Clustering (ICCC) algorithm is also proposed for two-way co-clustering. The experiment is performed using the advertisement and user data from Morgenstern, a financial social website that focuses on the agency of advertisements. The result shows that iterative cross co-clustering provides better performance than traditional clustering and completes the task more efficiently. 出版者：社團法人中華民國計算語言學學會出版日期： 2012-01-01 出處： Journal of Information Science and Engineering, 2012-01, Vol.28 (1), p.83-97 資源來源： CEPS中文電子期刊識別號： ISSN: 1016-2364 識別號： DOI: 10.6688/JISE.2012.28.1.6
显示于类别:	[資訊工程學系] 期刊論文

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