Mining browsing behaviors for objectionable content filtering
Journal
Journal of the Association for Information Science and Technology
Journal Volume
66
Journal Issue
5
Pages
930-942
Date Issued
2015
Author(s)
Abstract
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. ? 2014 ASIS&T.
Subjects
collaborative filtering
SDGs
Other Subjects
Electronic document exchange; Filtration; Hidden Markov models; Markov processes; Websites; Aggregation model; Browsing behavior; Clickthrough data; Content filtering; Contextual relationships; False positive rates; Large scale experiments; Top level domains; Collaborative filtering
Type
journal article