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Massive Internet invasions implemented through the distributed platform fabricated by rapid diffusion of malwares, has become a significant issue in network security. We argue that the notion of “Collaborative Security” is an emerging trend in resisting distributed attacks originated from malware. Therefore, this paper proposes a new architecture: CloudSEC, for composing collaborative security-related...
Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems. Recommender system has the access to adopt tagging systems to achieve better performance. In this paper we consider that the items can be categorized into different classifications in which users show different interests. Here...
Recommender system emerges as a technology addressing "information overload" problem. Collaborative Filtering (CF) is successful and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper...
Collaborative filtering, one of the most widely used algorithm in recommender system, predicts a user's preference towards an item by aggregating ratings given by users having similar taste with that user. State-of-the-art approaches introduce many other secondary methods to combine to cope with sparsity and precision problem. However, these hybrid approaches rarely consider the importance of context...
Recommender is a personalized service in the adaptive information system, and it can provide personalized information according to individual information needs. As one of the known technology in the field of the recommender systems, collaborative filtering has been widely used in E-Commerce for its advantages. But the rating prediction mechanism of pure collaborative filtering is merely based on the...
Collaborative filtering is one of the most successful technologies for building recommender systems, and is extensively used in many personalized systems. However, existing collaborative filtering algorithms have been suffering from data sparsity and scalability problems which lead to inaccuracy of recommendation. In this paper, we focus the collaborative filtering problems on two crucial steps: (1)...
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