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Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. <i>Lies and Propaganda</i> may be spread by a malicious user who may have an interest in promoting an item, or(More)
With the increasing popularity of recommender systems in commercial services, the quality of recommendations has increasingly become an important to study, much like the quality of search results from search engines. While some users faithfully express their true opinion, many provide noisy or incorrect ratings which can be detrimental to the quality of the(More)
Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam generation have posed a new challenge: image-based spam. Image based spam is email which includes embedded images containing the spam messages, but in binary format. In this paper,(More)
Current developments on Service-oriented Architectures, Peer-to-Peer and Grid computing promise more open and flexible architectures for digital libraries. They will open DL technology to a wider clientele, allow faster adaptability and enable the usage of federative models on content and service provision. These technologies rise new challenges for the(More)
Recommender systems have been steadily gaining popularity and has been deployed by several service providers. Large scalable deployment has however highlighted one of the design problems of rec-ommender systems: lack of interoperability. Users today often use multiple electronic systems offering recommendations, which cannot learn from one another. The(More)