一种优化标签的矩阵分解推荐算法

个性化推荐研究中，垃圾标签不仅会导致数据稀疏性问题，同时影响推荐的实时性和精确性。因此提出一种优化标签的矩阵分解推荐算法OTMFR，该算法分为两个阶段：首先优化标签，在建立三部网络图的基础上提出一种标签排序算法，利用互增强的关系得到关于标签流行度的排序，去除排序靠后的垃圾标签；然后在此基础上利用用户和资源对标签的偏好信息构建用户-资源偏好矩阵，并从矩阵分解的角度为用户产生推荐。在Delicious数据集上的实验结果表明，该算法在推荐精准度上有较为明显的效果。

Matrix factorization recommendation algorithm of optimizing tags

In personalized recommendation, garbage tags not only lead to the problem of data sparsity, but also affect both timeliness and accuracy of recommendation. Therefore, this paper presents a matrix factorization recommendation algorithm of optimizing tags OTMFR, which is divided into two stages：first optimizing tags, a tags sorting algorithm is proposed based on establishing　tripartite graphs network, which gets tags popularity ranking by making use of enhanced mutual relations and removes garbage tags those rank rearward. Then it uses users’ and resources’ preference information on tags to build the user-resource preference matrix, and generates recommendations for users from the perspective of matrix factorization. The experimental results on the dataset of Delicious show that the algorithm has better recommendation results.