基于用户复杂网络特征分类的协同过滤模型

协同过滤算法被广泛应用的同时一直存在着伸缩性和可扩展性困难的问题。针对该问题，提出了一种基于用户复杂网络特征分类的推荐系统协同过滤模型。首先，在用户集中基于度值选择特征用户，建立相似性阈值实现非特征用户分组；然后，构建用户—用户相似性网络，通过K-core分解完成网络中的社区标记；最后，目标用户在组内选择邻居，实现电影评分预测。基于MovieLens和Netflix数据集的实验结果表明，该算法与经典协同过滤算法相比，提升了时间和空间的性能，展现了更为出色的伸缩性和可扩展性。

Collaborative filtering model based on user complex network feature classification

Although collaborative filtering algorithms become widely applicable, they also suffer from the difficulties of scalability and extensibility. To address this problem, this paper proposed a collaborative filtering model for recommendation systems based on the classification of user complex network features(UCNFC). Firstly, the method selected feature users based on degree values in the user set and established similarity thresholds to implement groups of non-feature users. Then, user-user similarity constructed the network and completed the community labeling in the network by K-core decomposition. Finally, the target users choosed their neighbors within themselves group to accomplish movie rating prediction. Experimental results of MovieLens and Netflix datasets show that the proposed method improves both temporal and spatial performance compared with the classical collaborative filtering algorithm, is more excellent scalability and extensibility.