基于用户偏好和项目属性的协同过滤推荐算法

协同过滤推荐算法是目前应用最为广泛的个性化推荐方法之一，但传统的推荐算法在计算目标用户邻居集时只考虑用户项目评分矩阵中的具体数值，没有考虑用户偏好以及用户评分与项目属性之间的关系，推荐精度也有待进一步提高。针对这一问题，提出了一种基于用户偏好和项目属性的协同过滤推荐算法(UPPPCF)。本算法在传统的用户项目评分矩阵基础上综合考虑用户偏好以及项目属性，把评分矩阵转变成基于用户偏好的用户项目属性评分矩阵，然后根据这一评分矩阵来计算目标用户的最近邻居集，克服了传统相似性计算方法只依靠用户评分值的不足，同时本文对预测值判定给出了一种有效的度量方法。在 MovieLen 数据集上的实验结果表明，本文提出的UPPPCF算法能够有效弥补传统协同过滤算法中的不足，而且在推荐精度上有了明显的提高。

Collaborative Filtering Recommendation Algorithm Based on User Preferences and Project Properties

Collaborative filter algorithm is one of the most widely used technologies of personalized recommendation. However, the existing recommendation algorithms only consider the user item rating matrix specific value while calculating the target user neighbor. User preferences and user ratings and the relationship between the project properties are ignored. Moreover, the accuracy also needs to be further improved. To solve this problem, this paper proposed a new collaborative filtering algorithm based on user preferences and project properties (UPPPCF). By using the traditional user project evaluation matrix, the algorithm synthesizes user preferences and the project properties. The project score matrix is changed into project properties score matrix based on user preference. Then the nearest neighbors of target user sets are computed according to this new score matrix. As a result, the proposed algorithm overcomes the insufficiency of existing similarity calculation methods, which only depend on user ratings value. Meanwhile, an effective measurement method for predictor decision is suggested in this paper. The experimental results on MovieLen datasets show that the proposed algorithm can effectively improve the existing traditional collaborative filtering. In addition, the recommendation accuracy has been significantly improved.