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 thesauruses 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 done using two criteria of precision and recall based on
evaluations with "this http URL" dataset. The evaluation results show that the
precision and recall of the proposed method have significantly improved,
compared to the other methods.

使用数学和统计方法通过词汇相似度和共现标签来解决标签的语义相似性问题，建立了基于标签相似性的社区，并使用社区检测方法来确定用户的兴趣，从而改善推荐算法的效果。

基于图的推荐系统结合社区检测的增强方法

Graph-Based Recommendation System Enhanced with Community Detection

The empirical validation of community detection methods is often based on
available annotations on the nodes that serve as putative indicators of the
large-scale network structure. Most often, the suitability of the annotations
as topological descriptors itself is not assessed, and without this it is not
possible to ultimately distinguish between actual shortcomings of the community
detection algorithms on one hand, and the incompleteness, inaccuracy or
structured nature of the data annotations themselves on the other. In this work
we present a principled method to access both aspects simultaneously. We
construct a joint generative model for the data and metadata, and a
nonparametric Bayesian framework to infer its parameters from annotated
datasets. We assess the quality of the metadata not according to its direct
alignment with the network communities, but rather in its capacity to predict
the placement of edges in the network. We also show how this feature can be
used to predict the connections to missing nodes when only the metadata is
available, as well as missing metadata. By investigating a wide range of
datasets, we show that while there are seldom exact agreements between metadata
tokens and the inferred data groups, the metadata is often informative of the
network structure nevertheless, and can improve the prediction of missing
nodes. This shows that the method uncovers meaningful patterns in both the data
and metadata, without requiring or expecting a perfect agreement between the
two.

本文提出了一种用于评估社区检测算法的数据和元数据的联合生成模型及其参数推断方法，通过预测网络中边的位置来评估元数据的质量，并且展示了如何利用这个特点来预测缺失的节点和元数据。研究结果表明，尽管数据组和元数据标记之间很少存在精确的一致性，但元数据通常仍可以提供网络结构的信息，并且可以提高缺失节点的预测能力。