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 the 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.

中文翻译：

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通过社区检测增强的基于图的推荐系统

许多研究人员已经使用标签信息来提高推荐系统中推荐技术的性能。检查用户的标签将有助于了解他们的兴趣并导致推荐更加准确。由于用户定义的标签是自由选择的，没有任何限制，因此在确定其确切含义和标签的相似性时会出现问题。然而，由于用户自由定义以及许多数据集中使用不同的语言，使用同义词库和本体来查找标签的含义并不是很有效。因此，本文采用数学和统计方法来确定词汇相似度和共现标签解决方案来分配语义相似度。另一方面，由于用户兴趣随时间的变化，本文考虑了共现标签中标签分配的时间来确定标签的相似度。然后根据标签的相似性创建图表。为了对用户的兴趣进行建模，使用社区检测方法来确定标签的社区。这样，就完成了基于标签社区和资源之间相似性的推荐。通过对两个公共数据集的评估，使用精度和召回率两个标准来评估所提出方法的性能。评估结果表明，与其他方法相比，该方法的准确率和召回率都有显着提高。根据实验结果，查全率和查准率标准平均分别提高了 5% 和 7%。