一种基于项目属性评分的协同过滤推荐算法

协同过滤是电子商务推荐系统中应用最成功的推荐技术之一,但面临着严峻的用户评分数据稀疏性和推荐精度低等问题。针对数据稀疏性高和单一评分导致的推荐精度低等问题,提出一种基于项目属性评分的协同过滤推荐算法。首先通过均值法或缩放法构造用户-项目属性评分矩阵将单一评分转化为多评分;其次基于每个属性评分矩阵,计算用户间的偏好相似度,得到目标用户的偏好最近邻居集;然后针对每个最近邻居集,在用户-项目评分矩阵上完成对目标用户的初步评分预测;最后,将多个初步预测评分加权求和作为综合评分,完成推荐。在Movie Lens扩展数据集上的实验结果表明,该算法能有效提高推荐精度。

A collaborative filtering recommendation algorithm based on ratings of item attributes

Collaborative filtering is one of the most successful techniques in E commerce recommender system. However, it faces severe problems of sparse user ratings and low recommendation accuracy. To solve the problems of lower recommendation quality caused by rating data sparseness and single rating, we propose a collaborative filtering recommendation algorithm based on ratings of item attributes. Firstly, we construct user item attribute rating matrices using the mean value method or scaling method to transform single rating to multi rating. Based on each rating matrix of attributes, we then calculate the similarity among users to obtain the preference set of the nearest neighbors, and accomplish a primary prediction for each set of the nearest neighbors based on user item rating matrices. Finally, we calculate the weighted sum of multiple primary predictions as the final scores, and then complete the recommendation. The experimental results on the extended datasets of Movie Lens show that the proposed algorithm can get higher recommendation accuracy than traditional algorithms.