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With the development of the Internet, e-commerce industry rises rapidly. Online shopping becomes more and more convenient and fast. However it is very difficult for consumers to find satisfied commodity because of abundant and mixed commodity. Especially when people purchase the items which they are not familiar with or consume in a strange place. The study of the recommended system is to figure out...
Currently, most recommender systems are using collaborative filtering (CF) techniques. The main idea is to suggest new relevant items for an active user based on the judgements from other members in the like-minded community. However, these CF-based methods encounter the obstacles, such as sparse data, cold-start and robustness. This paper proposes to deal with these issues by associating similarity...
Similarity-based collaborative filtering systems are vulnerable to the data sparsity, cold-start, and robustness problems. Computational trust models are promising alternative solutions to alleviate these problems by replacing similarity metric with trust metric. However, they often have some shortages that rely on users' explicit trust statements. A fine-grained model computing trust from user ratings...
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