Personalized rough-set-based recommendation by integrating multiple contents and collaborative information

In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To this end, so called recommender systems are proposed to discover the implicit interests in user's mind based on the usage logs. However, the existing recommender systems suffer from the problems of cold-start, first-rater, sparsity and scalability. To alleviate such problems, we propose a novel recommender, namely FRSA (Fusion of Rough-Set and Average-category-rating) that integrates multiple contents and collaborative information to predict user's preferences based on the fusion of Rough-Set and Average-category-rating. Through the integrated mining of multiple contents and collaborative information, our proposed recommendation method can successfully reduce the gap between the user's preferences and the automated recommendations. The empirical evaluations reveal that the proposed method, FRSA, can associate the recommended items with user's interests more effectively than other existing well-known ones in terms of accuracy.