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Collaborative filtering (CF) is the most widely used technique for recommender systems. However, user similarity alone is not enough for recommendation. We propose that trust is another important issue in recommender systems. Due to data sparsity of the item ratings matrix, we may not find the similar neighbors of the active user and thus CF recommender systems often fails in this condition. Taking...
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