协同过滤推荐中一种改进的信息核提取方法

针对协同过滤推荐算法中存在的可扩展性问题，在原有基于频率（frequency-based，FB）和排名（rank-based，RB）的信息核提取方法的基础上，提出了改进的提取信息核方法IFB（IFrequency-based）和IRB（IRank-based，IRB），在寻找最相似邻居环节中提出了一个优化集的概念，在优化集上为每个用户寻找最相似的邻居。从实验结果看出，通过该方法能够得到更加准确的推荐结果，有效降低了绝对平均误差（MAE），同时具有更高的准确率和召回率，推荐效果更优。

Improved extraction method of information core in collaborative filtering recommendation

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 proposed an improved extraction information core method IFB(IFrequency-based) and IRB(IRank-based). When in search of the most similar neighbors, it proposed a concept: optimization set, and found the most similar neighbors for each user on this set. The experimental results show 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.