Content-Based Multicast is a type of multicast where the source sends a set of diierent classes of information and not all the subscribers in the multicast group need all the information. Use of ltering publish-subscribe agents on the intermediate nodes was suggested 5] to lter out the unnecessary information on the multicast tree. However, lters have their own drawbacks like processing delays ...

Content Based Multicast is a type of multicast where the source sends a set of di erent classes of information and not all the sub scribers in the multicast group need all the information Use of ltering publish subscribe agents on the intermediate nodes was suggested to lter out the unnecessary information on the multicast tree However lters have their own drawbacks like processing delays and i...

Collaborative filters are frequently used in e-commerce to provide a heightened user experience and to tempt users into making purchases by recommending items and drawing the user’s attention to additional products. Purchasing of digital media over the Internet continues to be popular and e-commerce giants such as Amazon.com, CDNOW.com and Launch.com heavily employ Automated Collaborative Filte...

Content filtering through keyword matching is widely adopted in network censoring, and proven to be successful. However, a technique to bypass this kind of censorship by decomposing Chinese characters appears recently. Chinese characters are combinations of radicals, and splitting characters into radicals pose a big obstacle to keyword filtering. To tackle this challenge, we proposed the first ...

Recommender systems use several of data mining techniques and algorithms to identify user preferences of items in a system out of available millions of choices. Instead of providing a static experience in which users search for and buy products, recommender systems help to increase interaction to provide a richer experience. Recommender systems can easily identify the recommendations autonomous...

Web servers dominate our view of the Web today. Security provided by them has been implemented with varying degrees of success. Web servers are frequently successfully attacked, with subsequent loss of corporate loss of face or revenue. Recent legislation has increased the importance of ensuring that only approved users gain access to information, which often implies filtering content served by...

Designing a spam-filtering system that can run efficiently on heavily burdened servers is particularly important to the widely used email service providers (ESPs) (e.g., Hotmail, Yahoo, and Gmail) who have to deal with millions of emails everyday. Two primary challenges these companies face in spam filtering are efficiency and scalability. This study is undertaken to develop an efficient and sc...

In this work we are concerned with the detection of spam in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting spam in videos. This is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, what is pa...

In the view of massive content explosion in World Wide Web through diverse sources, it has become mandatory to have content filtering tools. The filtering of contents of the web pages holds greater significance in cases of access by minor-age people. The traditional web page blocking systems goes by the Boolean methodology of either displaying the full page or blocking it completely. With the i...

Recommendation systems have been studied actively since the 1990s. Generally, recommendation systems choose one or more candidates from a set of candidates through a filtering process. Methods of filtering can be divided into two categories: collaborative filtering, in which candidates are chosen based on choices of other persons whose interests or tastes are similar, and content-based filterin...