结合用户聚类和项目类型的协同过滤算法

为了解决协同过滤算法中数据稀疏性问题，提高推荐效果，提出一种改进的协同过滤算法.该算法首先通过一种新的相似度计算方法来计算项目类型相似度，将相似度大于某阈值的项目作为目标项目的邻居；然后根据目标用户对邻居项目的评分信息来预测该用户对目标项目的评分值，并将预测值填入稀疏的用户项目评分矩阵；最后对填充后的评分矩阵采用基于用户聚类（K-means聚类）的协同过滤算法做出最终的预测评分进行推荐.在Movielens数据集上进行实验验证，结果表明该算法能够很好地缓解数据稀疏性、降低计算复杂度，提高推荐精度.

Collaborative Filtering Algorithm Combined with the User Clustering and Item Types

In this paper,in order to solve the problem of data sparseness and improve the effect of recommendation,an improved collaborative filtering algorithm is put forward.Firstly,this algorithm calculates the item-types similarities through a new calculation method and the items whose similarities are greater than a certain threshold value will be considered as neighbors of the target-item.Secondly,the system predicts target-user''s score values for the target-item according to the scores for the neighbors of target-item,and the predicted values will be filled in the sparse score matrix.Finally,this algorithm clusters the new matrix (K-means clustering) based on the users,to predict target-user''s score values and make recommendations.The experimental results on the Movielens dataset show that this algorithm can effectively alleviate the data sparseness,reduce the computational complexity and improve recommendation accuracy.