融合信任关系和用户项目二部图的推荐算法

传统冷启动和数据稀疏性问题是推荐系统面临的两大难题。现有的大多数基于矩阵分解的推荐方法将用户孤立对待，忽略了用户之间的信任关系，从而导致推荐性能低效。提出一种融合信任关系和用户项目二部结构的矩阵分解推荐方法。该方法在对评分矩阵进行分解的基础上，加入用户信任关系和用户项目二部图结构信息，采用梯度下降算法训练模型参数。Epinions数据集上的对比实验表明，该方法有效提高了推荐系统的准确性和可靠性，尤其在冷启动和稀疏数据情况下，其推荐精度明显优于传统的推荐方法。

Incorporating social trust relationship and bipartite network for recommendation

Cold-start and data sparsity issues have still been two challenges in recommender systems. In most of traditional recommender systems based on the matrix factorization model, it is often assumed that users are isolated and the relationships among users are ignored, this results in the decrease in the recommendation effects. Thus, a novel approach incorporating social trust relationship and the structure of bipartite network is proposed. Based on the matrix factorization, this proposed approach combines the social trust relationships among users with the structure of bipartite network, and employs the gradient algorithm to train model parameters. The experimental results on Epinions data set show that the proposed approach is superior to other advanced approaches in accuracy and reliability, especially while the cold-start and data sparsity issues are involved in.