基于用户偏好动态变化的协同过滤推荐

传统的协同过滤方法利用用户评分数据来生成推荐，没有考虑评价时间和项目类别等其他信息，影响了系统推荐的质量。本文提出一种基于用户偏好动态变化的个性化推荐模型，该方法在基于项目类别的基础上，为用户评分时间距离现在较近、较远和周期性评分分别赋予不同的权重。从MovieLens数据集的实验结果表明，该方法消弱了历史短期偏好对推荐质量的影响，准确地反映了用户偏好的动态变化，有效地提高了推荐的准确性。

Collaborative Filtering Recommendation Based on Dynamic Changes of User Preferences

Traditional collaborative filtering methods focus only on rating data to generate recommendation,without considering the evaluation time,project category and other informations,which affects the quality of the recommended system. This paper proposes a personalized recommendation model based on the dynamic changes of user preferences. The method is based on the project category,and different weighting functions are set up according to the user scoring time( recent,long and periodic). The experimental results from Movielens data set show that this method weakens the influence of short-term preference on recommendation quality,reflects the dynamic changes of user preferences accurately,and improves the accuracy of recommendation effectively.