关联性动态加权的协同过滤推荐

利用传统的协同过滤（CF）算法进行推荐时，由于用户评分矩阵比较稀疏，直接得到的用户或者项目之间的相似度相对而言可信度就比较低。为了解决这个问题，在传统的协同过滤基础上，引入项目与项目之间的关联性，通过在项目的类别标签和二部图的方法之间构建动态权重因子来融合这两种关联，形成非对等关联性关系，并做出用户对项目的评分预测，从而解决评分矩阵过于稀疏的问题。研究结果表明，相比于传统方法中使用对等相似度关系以及固定权值的方法，通过动态权重融合关联性形成非对等的关系的方法，更贴合生活实际，并且有更好的推荐效果。

Relevance dynamic weighted collaborative filtering recommendation

When the traditional collaborative filtering algorithm is used for recommendation, the credibility based on similarity between users or items directly obtained is relatively low due to the sparsity of the user rating matrix. In order to solve this problem, this paper introduced the relevance between projects on the basis of traditional collaborative filtering. It established the association in a non-reciprocal condition by building a dynamic weighting factor between the project''s category label and bipartite graph approach, and the user''s rating of the project would be predicted. As a result, compared with the method of using the equivalent similarity and the fixed weight value in the traditional method, the method of using non-equivalent relationship formed by the dynamic weights is more in line with the reality of life and has a better recommendation effect.