Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation
作者： Bamshad Mobasher , Robin Burke , Yong Zheng
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摘要: Research in context-aware recommender systems (CARS) usually requires the identification of influential contextual variables advance. In collaborative recommendation, there is a substantial trade-off between applying context very strictly and achieving good coverage accuracy. Our prior work showed that this tradeoff can be managed by contexts differentially different components recommendation algorithm. paper, we extend our previous model show differential relaxation (DCR) also used to identify demographic item features are linked contexts. We demonstrate application binary particle swarm optimization as scalable technique for deriving optimal relaxation.
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