分步筛选邻居的协同过滤改进算法

Collaborative Filtering Recommendation Algorithm with Step Screening Neighbors

为了解决协同过滤算法用户邻居筛选的优化问题,提高推荐结果的准确性,提出了一种分步筛选邻居的协同过滤改进算法.该算法首先采用改进的Pearson系数法计算用户间的相似度,降序排列后,计算用户特征值,大于用户特征阈值的用户进入下一层筛选;然后选择对优先项目集有过评分的用户形成最终的邻居集;最后进行预测评分得到推荐.实验结果表明,该算法能够有效地获取用户最近邻居集,改善准确性,并且稳定性良好.

To increase the accuracy of the neighbor screening in collaborative filtering algorithm, an improved system-collaborative filtering with step screening neighbors （SSN-CF）-is proposed in this paper. This algorithm firstly uses an improved Pearson method to compare the similarity between users. After arranging the data in descending order, the uses＇ characteristic value is calculated. Only those who surpass the threshold value are selected. Then the system gathers the users who graded the priority set to make up the final neighbor set. Finally the users＇ grades are estimated and recommendation is made. Experiments have shown that the algorithm can effectively get the most similar neighbor set of target uses. Meanwhile, it is tested that accuracy and stability is improved.