关键词: 标签, 网络图, 互增强, 偏好信息, 矩阵分解

Abstract: 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.

Key words: tags, graphs network, enhanced mutual relations, preference information, matrix factorization