基于增强二部图网络结构的推荐算法

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.基于用户-项目二部图的信任计算可以有效的利用用户间的潜在联系提高推荐性能.提出一种融合基于二部图的增强繁殖信任与JMSD相关系数的推荐方法，包括对改进的加权用户-项目自适应繁殖信任度的计算，在此基础上融合用户偏好的增强信任度机制，以及线性加权JMSD相关系数，两组数据集下的对比实验表明，与三种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），更高的召回率（Recall），提高了推荐质量.

Recommendation Algorithm Based on Enhanced Bipartite Graph Network

The data sparsity and cold start problem of collaborative filtering recommendation algorithm affects and constrains the quality of recommendation. Trust calculation based on user-project bipartite graph can effectively utilize the hidden connection between users. The algorithm model proposed in this study includes the calculation of improved user-project adaptive trust, incorporates an improved enhanced trust mechanism based on user preferences, linear weighted JMSD correlation coefficient. Experiments under the two sets of data set show that the improved algorithm model has a lower Mean Absolute Error (MAE) and higher recall rate (Recall) than the three benchmark algorithms, both models have their own focus, which improves the quality of recommendations.