基于均衡接近度灰关联的增强二部图推荐算法

协同过滤推荐算法的数据稀疏性与冷启动问题影响和制约了推荐的质量，传统用户—项目二部图信任和相似度计算受局部个别点关联因素的消极影响。首先提出一种基于均衡接近度灰关联方法计算项目流行度的二部图信任推荐，在此基础上提出用户偏好的增强二部图直接信任度机制，然后通过JMSD相关系数作为全局信任推荐。在MovieLens数据集下的对比实验表明，与基准算法对比改进的算法模型具有更低的平均绝对误差（MAE），提高了推荐质量，改善了冷启动问题。

Enhanced bipartite graph recommendation algorithm based on grey correlational analysis by degree of balance and approach

The data sparsity and cold start problems of the collaborative filtering recommendation algorithm affect the quality of the recommendation. Traditional user-item bipartite graph trust and the similarity calculation is negatively affected by the local individual point correlation factors. Firstly, this paper proposed a bipartite graph trust recommendation based on project popularity calculated by a grey correlational analysis by degree of balance and approach. On this basis, it proposed the user''s preference enhanced bipartite graph direct trust degree mechanism, and used the JMSD correlation coefficient as the global trust recommendation. The comparison experiments under the MoiveLens dataset show that the improved algorithm has a lower mean absolute error(MAE) compared with the benchmark algorithm, it improves the recommendation quality and alleviates the cold start problem.