基于商品属性值和用户特征的协同过滤推荐算法

为了提高用户相似度计算精度和推荐准确性,缓解数据稀疏性,提出一种基于商品属性值和用户特征的协同过滤推荐算法。该算法首先从用户对商品属性值的偏好出发,计算用户对商品属性值的评分分布和评分期望值,得到用户-属性值评分矩阵;同时利用数据相似性度量方法寻找用户特征邻居,填充用户-属性值评分稀疏矩阵,进而得出目标用户偏好的最近邻居集;计算用户对未评属性值的评分,将目标用户对商品所有属性值评分的均值进行排序,形成该用户的Top-N推荐列表。采用Movie Lens和Book Crossing数据集进行实验,结果表明该算法在缓解数据稀疏性问题上效果较好,推荐精度显著提高。

A collaborative filtering recommendation algorithm based on item attribute values and user characteristics

In order to improve the precision of similarity calculation and recommendation accuracy and reduce data sparseness, we propose a collaborative filtering recommendation algorithm based on item attribute values and user features. Firstly, based on the user preference for item attribute values, we calculate the rating distribution of item attribute values and rating expectations, and obtain the user-attribute value rating matrix. In the meantime, we use a data similarity measure method to find user characteristics neighbors and fill the sparse user-attribute value rating matrix, thus obtaining the preference set of the nearest-neighbors. Thirdly, we calculate the rating of the unrated attribute values, and sort the means of the rating of all item attribute values, thus obtaining a Top-N recommendation list for the target user. Experiment on the Movie Lens data set and Book Crossing data set show that the algorithm can better overcome the data sparsity problem and enhance recommendation accuracy.