基于用户项目体验度的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on User Project Experience Degree

在用户相似度计算基础上,根据用户偏好以及项目特征对用户评分产生的影响,提出一种针对用户项目体验度的推荐算法。阐述项目体验度对用户产生的潜在影响,选择皮尔森相似性计算公式做进一步计算。通过用户对项目的好评数以及给项目的评分分别占该项目的总评数和总体项目评分中的比例,获得用户对项目的体验度权重。采用长尾理论平衡用户相似性和用户对流行项目的关注度,计算得出用户相似度并产生预测和推荐。实验结果表明,与传统协同过滤算法相比,该算法提高了相似度计算准确度,并能改善数据稀疏情况下的推荐效果。

On the basis of the general user＇ s similarity calculation, a recommendation algorithm based on user project experience degree is proposed according to the impact of user preferences and project characteristics on user ratings. This paper describes the potential impact of the project experience degree on the user, and selects Pearson similarity formula for further calculation. Through the number of users praise for the projects accounting for the total number of project reviews and the proportion of the user＇ s rating to the overall project score,the user experience weight for the project is gained. The long tail theory is used to balance the user＇ s similarity and the user＇ s attention to the popular items. The user＇ s similarity is calculated, and the prediction and recommendation are generated. Experimental results show that, compared with the traditional collaborative filtering algorithm, the proposed algorithm improves the accuracy of similarity computation and the recommendation effect under sparse data.