【摘要】 传统协同过滤推荐算法存在数据稀疏的问题，这会导致算法精确度不足。Slope＿One算法简单高效，可以预测用户对某个物品的评分。因此，论文提出融合动态K近邻Slope＿One的协同过滤推荐算法，提高推荐算法的精确度。首先利用改进余弦相似度公式计算用户相似度，筛选出K个近邻用户进行平均评分偏差计算，利用Slope＿One算法预测相应的用户评分并对评分矩阵进行有效填充，然后在新的评分矩阵上，利用基于物品的协同过滤算法进行推荐。更多还原

【Abstract】 Data sparse is a problem of traditional collaborative filtering algorithm,which will cause the algorithm to be insufficient. The Slope＿One algorithm is simple and efficient,and can predict the user’s rating of an item. Therefore,this paper proposes a collaborative filtering recommendation algorithm combining dynamic K-nearest neighbor Slope＿One to improve the accuracy of the algorithm. First,the improved cosine similarity formula is used to calculate the user similarity,K neighbor users are screened to calculate the average score deviation,the Slope＿One algorithm is used to predict the corresponding user score,and effectively the score is filled into data matrix,and then the item-based collaborative filtering algorithm is used for recommendation.更多还原