结合信任度和项目关联的混合推荐算法

推荐系统是大数据时代处理信息过载问题的重要手段,传统的推荐算法的准确性和可靠性相对较低。针对用户和项目冷启动问题,提出一种基于概率矩阵分解的混合型推荐算法(HR-TP),先从用户的评分角度挖掘用户的信任关系,再利用标签上下文根据用户特征测量项目间的关联关系,然后融合到概率矩阵模型中进行推荐。实验表明,本文提出的算法在推荐精度上对比常规方法取得了很好的效果。

A hybrid recommendation algorithm combiningtrust degree and project association

Recommendation is an important means of dealing with information overload in the era of big data. Traditional recommendation algorithms have relatively low accuracy and reliability. Aiming at the cold start issue of new users and projects, we propose a hybrid recommendation algorithm based on probability matrix decomposition (HR-TP). Firstly, the user's invisible trust relationship is mined from the perspective of the user’s rating. Then the label context is used to measure the relationship between items according to user characteristics. The relationship matrix is fused with the probability matrix model to make recommendation. Experiments show that the proposed method achieves better results in recommendation accuracy compared with conventional methods.