协同过滤推荐中一种改进的信息核提取方法

Recommender systems (RS) help users to find interesting information in plenty of data resources, and provide accurate personalized recommendation. While the recommendation algorithm based on information core can greatly reduce the time cost in the recommendation process. Aiming at the scalability problem in collaborative filtering recommendation algorithm, On the basis of the original information core extraction method based on frequency (frequency-based, FB) and ranking (rank-based, RB) , this paper proposes an improved extraction information core method IFB (IFrequency-based) and IRB(IRank-based) . When in search of the most similar neighbors, we proposed a concept : optimization set, and found the most similar neighbors for each user on this set. The experimental results showed that this method can get more accurate recommendation results, and reduce the mean average absolute error(MAE) effectively. At the same time, it has higher precision and recall, so it has better recommendation effect.