Mining browsing behaviors for objectionable content filtering

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/107774

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107774

Title:	Mining browsing behaviors for objectionable content filtering
Authors:	李龍豪;Lee, Lung-Hao;Juan, Yen-Cheng;Tseng, Wei-Lin;Chen, Hsin-Hsi;Tseng, Yuen-Hsien
Contributors:	資訊電機學院電機工程學系
Keywords:	Agglomeration;Browsers (computer);Browsing;Categories;collaborative filtering;Filtering;Filtration;Mathematical models;Websites
Date:	2015-05-01
Issue Date:	2026-04-23 14:25:37 (UTC+8)
Publisher:	John Wiley and Sons Ltd;Blackwell Publishing Ltd
Abstract:	摘要： This article explores users' browsing intents to predict the category of a user's next access during web surfing and applies the results to filter objectionable content, such as pornography, gambling, violence, and drugs. Users' access trails in terms of category sequences in click‐through data are employed to mine users' web browsing behaviors. Contextual relationships of URL categories are learned by the hidden Markov model. The top‐level domains (TLDs) extracted from URLs themselves and the corresponding categories are caught by the TLD model. Given a URL to be predicted, its TLD and current context are empirically combined in an aggregation model. In addition to the uses of the current context, the predictions of the URL accessed previously in different contexts by various users are also considered by majority rule to improve the aggregation model. Large‐scale experiments show that the advanced aggregation approach achieves promising performance while maintaining an acceptably low false positive rate. Different strategies are introduced to integrate the model with the blacklist it generates for filtering objectionable web pages without analyzing their content. In practice, this is complementary to the existing content analysis from users' behavioral perspectives. 其他題名： J Assn Inf Sci Tec 出版者： Blackwell Publishing Ltd 出版日期： 2015-05 出處： Journal of the Association for Information Science and Technology, 2015-05, Vol.66 (5), p.930-942 資源來源： Wiley Online Library eJournals 版權： 2014 ASIS&T 識別號： ISSN: 2330-1635 識別號： EISSN: 2330-1643 識別號： DOI: 10.1002/asi.23217
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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