一种改进的协同过滤推荐算法

Improved Collaborative Filtering Recommendation Algorithm

传统的协同过滤算法在寻找最近邻居集合时没有考虑时间因素的影响,仅从用户或者项目单方面出发计算用户或者项目的相似性以产生推荐结果,也忽略了用户特征对推荐的影响。针对上述问题,引入时间遗忘函数、黏度函数、用户特征向量,对协同过滤算法寻找用户的最近邻居集合过程进行了改进,体现了时间效应、用户偏好程度和用户特征。采用MovieLens数据集进行了一系列对比实验,结果表明,改进后的算法能够明显提高推荐的准确度。

When searching the nearest neighbor set, the traditional collaborative filtering algorithm ignores the impact of the time factor, only from the user or item takes into account the similarity of the user or item unilaterally, and ignores the impact of user characteristics on reeommendatiorL Aiming to the above problems, we introduced the time forgotten function, resources viscosity function and the user feature vector, and improved the process of finding the user＇s nearest neighbor set, reflected the time effect, degree of user preferences and user characteristics, and tested this new methodology on data set got from MovieLens. The results of experiment show that proposed method can improve the accuracy of the prediction.