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A structural and content‐based analysis for Web filtering
Internet Research, 2003With the proliferation of objectionable materials (e.g. pornography, violence, drugs, etc.) available on the WWW, there is an urgent need for effective countermeasures to protect children and other unsuspecting users from exposure to such materials.
Pui Y. Lee +2 more
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Algorithms for Efficient Filtering in Content-Based Multicast
2001Content-Based Multicast is a type of multicast where the source sends a set of different classes of information and not all the subscribers in the multicast group need all the information. Use of filtering publish-subscribe agents on the intermediate nodes was suggested [5] to filter out the unnecessary information on the multicast tree.
Langerman, Stefan, Lodha, S., Shah, R.
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Twitter Content-Based Spam Filtering
2014Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However,
Igor Santos +5 more
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Content Based Spam E-mail Filtering
2016 International Conference on Collaboration Technologies and Systems (CTS), 2016Currently, E-mail is one of the most important methods of communication. However, the increasing of spam e-mails causes traffic congestion, decreasing productivity, phishing, which has become a serious problem for our society. And the number of spam e-mail is increasing every year.
Pingchuan Liu, Teng-Sheng Moh
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Evaluating content-based filters for image and video retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004This paper investigates the level of metadata accuracy required for image filters to be valuable to users. Access to large digital image and video collections is hampered by ambiguous and incomplete metadata attributed to imagery. Though improvements are constantly made in the automatic derivation of semantic feature concepts such as indoor, outdoor ...
Michael G. Christel +2 more
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A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review, 1999We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of ...
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Content-Based Image Filtering for Recommendation
2006Content-based filtering can reflect content information, and provide recommendations by comparing various feature based information regarding an item. However, this method suffers from the shortcomings of superficial content analysis, the special recommendation trend, and varying accuracy of predictions, which relies on the learning method. In order to
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What Happened to Content-Based Information Filtering?
2009Personalisation can have a significant impact on the way information is disseminated on the web today. Information Filtering can be a significant ingredient towards a personalised web. Collaborative Filtering is already being applied successfully for generating personalised recommendations of music tracks, books, movies and more.
Nikolaos Nanas +2 more
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Content-based network filtering of encrypted image data
2010 IEEE International Conference on Wireless Communications, Networking and Information Security, 2010The proliferation of multimedia encryption techniques allows securing various applications including tele-browsing and visio-conferencing. However, these techniques may also constitute useful tools for malicious users to transmit prohibited data without being detected by preventive and reactive security mechanisms.
Mohamed Hamdi, Noureddine Boudriga
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Selecting content-based features for collaborative filtering recommenders
Proceedings of the 7th ACM conference on Recommender systems, 2013We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the ``cold start'' problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization.
Royi Ronen +3 more
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