中文摘要：

[目的]利用用户标签及关系网络,为用户推荐潜在的相似用户。[方法]通过探究社会化标注系统中标签、关系网络所表征的用户长短期兴趣特征,综合用户标签及关注关系,利用多维尺度法构建用户聚类模型,根据用户聚类结果进行相似用户推荐,并以＂微博＂为例对模型进行实证。[结果]实验结果表明,基于标签和关系网络的用户聚类模型能够有效地结合用户长短期兴趣特征,挖掘潜在相似用户,聚类及推荐效果较好。[局限]样本数据集具有局限性,不能完全涵盖用户兴趣领域,仅从一个领域验证了模型的准确性与有效性。[结论]通过对用户标签及关系网络挖掘用户长短期兴趣,构建的基于用户静态标签与动态关系网络的用户推荐模型,对个性化用户推荐效果有较好的提升。

英文摘要：

[Objective] This paper proposes a new model to recommend potential similar users with the help of social tags and relation network. [Methods] First, we explored characteristics of the users＇ short or long-term interests based on the social tagging system. Then, we built a user-clustering model using multidimensional scaling method with the tags and relationship data. Finally, we recommended similar users based on the clustering results. The proposed model was examined with Weibo data. [Results] We found that the new model could effectively combine the characteristics of the user＇s interests, and then identify the potential similar ones. [Limitations] The sample data does not include everything on user interests. Thus, we only examined the effectiveness of the proposed model with limited data. [Conclusions] The user recommendation model based on static tags and dynamic relational network could improve the personalized recommendation services.