Attribute Reduction with Rough Set in Context-Aware Collaborative Filtering

The problem of different contextual information to influence the user-item-context interactions at varying degrees in context-aware recommender systems is addressed. To improve the performance accuracy, we develop a novel attribute reduction algorithm in order to effectively extract the core contextual information using rough set. We combine collaborative filtering with contextual information significance to generate more accurate predictions. We experimentally evaluate our approach on UCI machine learning repository and two real world data sets. Experimental results demonstrate that our proposed approach outperforms existing state-of-theart context-aware recommendation methods.