Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users' interests over time this article has considered the time of tag assignments in co-occurrence tags for determining similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7% respectively.

翻译：许多研究者利用标签信息来提升推荐系统中推荐技术的性能。分析用户标签有助于获取其兴趣，从而提高推荐的准确性。由于用户自定义标签具有自由且不受限制的特性，在确定其确切含义及标签间相似性时会产生问题。然而，因用户可随意定义标签且许多数据集涉及多种语言，使用叙词表和本体来推导标签含义的方法效率不高。因此，本文采用数学与统计方法确定词汇相似性，并利用共现标签方案来赋予语义相似性。另一方面，考虑到用户兴趣随时间的变化，本文在共现标签中纳入标签分配时间以确定标签相似性。随后基于标签相似性构建图结构。为建模用户兴趣，使用社区检测方法确定标签社区。最终基于标签社区及资源间相似性完成推荐。通过在两个公开数据集上使用精确率与召回率两个指标进行评估，所提方法的性能得到了验证。评估结果表明，与其它方法相比，所提方法的精确率和召回率均有显著提升。实验结果显示，召回率和精确率指标平均分别提升了5%和7%。