With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of them were considered in perspective of a single-layer network, which destroyed the original hierarchy of data and resulted in sparse matrix and poor timeliness. In order to address these problems and improve the accuracy of recommendation, dynamic clustering collaborative filtering recommendation algorithm based on double-layer network is put forward in this paper. Firstly, attribute information of users and items are respectively used to construct the user layer network and the item layer network. Secondly, new hierarchical clustering method is further presented, which separates users into different communities according to dynamic evolutionary clustering. Finally, score prediction and top-N recommendation lists are obtained by similarity between users in each community. Extensive experiments are conducted with three real datasets, and the effectiveness of our algorithm is verified by different metrics.

随着互联网经济的飞速发展，个人推荐系统在电子商务中的作用日益重要。为了提高推荐的质量，许多学者和工程师致力于开发推荐算法。传统的协作过滤算法仅依赖于评级信息或属性信息。从单层网络的角度考虑了其中大多数问题，该网络破坏了原始数据层次结构，导致矩阵稀疏和及时性差。为了解决这些问题，提高推荐的准确性，提出了一种基于双层网络的动态聚类协同过滤推荐算法。首先，用户和物品的属性信息分别用于构建用户层网络和物品层网络。其次，提出了一种新的层次聚类方法，根据动态进化聚类将用户分为不同的社区。最后，通过每个社区中用户之间的相似性获得分数预测和前N个推荐列表。我们使用三个真实的数据集进行了广泛的实验，并通过不同的指标验证了我们算法的有效性。得分预测和前N个推荐列表是通过每个社区中用户之间的相似性获得的。我们使用三个真实的数据集进行了广泛的实验，并通过不同的指标验证了我们算法的有效性。得分预测和前N个推荐列表是通过每个社区中用户之间的相似性获得的。我们使用三个真实的数据集进行了广泛的实验，并通过不同的指标验证了我们算法的有效性。