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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...
Geographic information service has been becoming the mainstream to provide GIS functions to users more conveniently. There are many geographic information services available among which some may have similar data and functions, thus how to choose the most proper and efficient one becomes the very problem. In recent years, more and more attention has been paid to the quality of geographic information...
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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