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

As the deep learning techniques have expanded to real-world recommendation
tasks, many deep neural network based Collaborative Filtering (CF) models have
been developed to project user-item interactions into latent feature space,
based on various neural architectures, such as multi-layer perceptron,
auto-encoder and graph neural networks. However, the majority of existing
collaborative filtering systems are not well designed to handle missing data.
Particularly, in order to inject the negative signals in the training phase,
these solutions largely rely on negative sampling from unobserved user-item
interactions and simply treating them as negative instances, which brings the
recommendation performance degradation. To address the issues, we develop a
Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is
capable of exploring transferable knowledge from observed and unobserved
user-item interactions. The network architecture of CRANet is formed of an
integrative structure with a reflective receptor network and an information
fusion autoencoder module, which endows our recommendation framework with the
ability of encoding implicit user's pairwise preference on both interacted and
non-interacted items. Additionally, a parametric regularization-based
tied-weight scheme is designed to perform robust joint training of the
two-stage CRANet model. We finally experimentally validate CRANet on four
diverse benchmark datasets corresponding to two recommendation tasks, to show
that debiasing the negative signals of user-item interactions improves the
performance as compared to various state-of-the-art recommendation techniques.
Our source code is available at this https URL.

文章提出了 Collaborative Reflection-Augmented Autoencoder Network (CRANet) 相关理论，旨在解决在训练阶段中由缺省数据引入的负信号所带来的推荐性能下降问题，并通过实验验证 CRANet 相比于其他推荐技术可以显著改善推荐问题的性能。

协同反思增强自编码网络用于推荐系统

Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems

Recommendation techniques are important approaches for alleviating
information overload. Being often trained on implicit user feedback, many
recommenders suffer from the sparsity challenge due to the lack of explicitly
negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the
challenge by learning a generator and a discriminator adversarially, such that
the generator produces increasingly difficult samples for the discriminator to
accelerate optimizing the discrimination objective. However, producing samples
from the generator is very time-consuming, and our empirical study shows that
the discriminator performs poor in top-k item recommendation. To this end, a
theoretical analysis is made for the GAN-style algorithms, showing that the
generator of limit capacity is diverged from the optimal generator. This may
interpret the limitation of discriminator's performance. Based on these
findings, we propose a Sampling-Decomposable Generative Adversarial Recommender
(SD-GAR). In the framework, the divergence between some generator and the
optimum is compensated by self-normalized importance sampling; the efficiency
of sample generation is improved with a sampling-decomposable generator, such
that each sample can be generated in O(1) with the Vose-Alias method.
Interestingly, due to decomposability of sampling, the generator can be
optimized with the closed-form solutions in an alternating manner, being
different from policy gradient in the GAN-style algorithms. We extensively
evaluate the proposed algorithm with five real-world recommendation datasets.
The results show that SD-GAR outperforms IRGAN by 12.4% and the SOTA
recommender by 10% on average. Moreover, discriminator training can be 20x
faster on the dataset with more than 120K items.

这篇论文提出了一种采样分解生成对抗式推荐器 (SD-GAR) 方法，旨在解决生成样本耗时以及推荐过程中负样本缺乏的问题。作者通过自规范化重要性采样以及分解采样来解决推荐中的挑战，并在五个真实的推荐数据集上进行了评估，结果显示 SD-GAR 的推荐效果比目前最先进的方法还要好。