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Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The huge volume of user-generated data typically results in heavyweight computations for even a single similarity update. We present I-SIM, a novel similarity...
Computing k-nearest-neighbor graphs constitutes a fundamental operation in a variety of data-mining applications. As a prominent example, user-based collaborative-filtering provides recommendations by identifying the items appreciated by the closest neighbors of a target user. As this kind of applications evolve, they will require KNN algorithms to operate on more and more sensitive data. This has...
We present WHATSUP, a collaborative filtering system for disseminating news items in a large-scale dynamic setting with no central authority. W HATS U P constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive (like-dislike). Users with similar tastes are clustered using a similarity metric reflecting long-standing and emerging...
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