缓解数据稀疏问题的协同过滤混合填充算法

现实评分矩阵非常稀疏,基于用户的协同过滤无法为目标用户提供高精度的满意推荐.基于此种情况,文中提出协同过滤混合填充算法,缓解数据稀疏问题.从物品角度出发,根据相似物品的评分信息填充稀疏矩阵.同时从用户角度出发,利用填充后的矩阵计算目标用户的邻近用户.选取共同评分数量最多的物品以进一步填充矩阵.在两个真实数据集上的实验表明,本文算法在无需额外复杂信息的条件下,有效提高新用户推荐的精确度,缓解数据稀疏性问题.

Collaborative Filtering Hybrid Filling Algorithm for Alleviating Data Sparsity

The rating matrix is sparse,and the traditional user-based collaborative filtering cannot provide high-precision satisfactory recommendations for target users.Based on this situation,a hybrid filling collaborative filtering(HFCF)is proposed to alleviate the problem of data sparsity.From the perspective of the item,the sparse matrix is filled according to the rating information of the similar items.And from the viewpoint of users,the neighborhood of the target users is calculated according to the filled matrix.The items with the largest number of common ratings are selected to further fill the matrix.Experiments on two real datasets indicate that the proposed algorithm effectively improves the recommendation precision and relieves the data sparsity problem without any additional information.