Abstract
Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users' similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.
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