## 2 Related Works  
In this era of internet and digitalization, an enormous amount of textual data is generated at a high rate. Text data analysis applications are widespread, starting from customer review analysis to extracting and finding a large dataset's hidden meaning. [PERSON] proposes a novel approach to recognize the topics, which ultimately led to sentiments classification, documents classification, and unlocked relatively many assessment prospects for textual data [5]. Topic models are of crucial importance for the illustration of discrete data and are used in different research fields such as medical sciences [6], software engineering [7], geography [8], and political sciences [9], etc. There are many topic modeling techniques; each has its strengths and limitations. The most frequently used approaches include latent semantic analysis (LSA) [10], probabilistic latent semantic analysis (PLSA) [11], latent Dirichlet allocation (LDA) [12], and correlated topic model (CTM) [13]. LSA's primary focus is to generate different representations of texts based on vectors to create semantic content [10,14]. These vector representations are designed to choose related words by computing the similarity among text data. LSA has many applications such as keyword matching, word quality assessment, power collaborative learning, guidance in career choices, making optimal teams [15], reduction of dimensions [16], and identification of research trends [17]. PLSA was introduced to fix the limitations of LSA [18]. It has many implications, including the differentiation of the words with several meanings and clustering of words that share similar contexts [19]. In [20], PSLA is introduced as an aspect model based on a latent variable responsible for linking observations with unseen class variables. In addition to introducing advancements in LSA, PSLA has many other applications, including recommender systems and computer vision [21, 22, 23]. LDA model aims to overcome the limitations of LSA and PSLA in capturing the exchangeability of document words. LDA being an unsupervised approach for topic modeling, has recently become very popular, mainly for topic discovery in a large corpus. In [24], LDA is used for text mining that is based on Bayesian topic models. LDA is also a generative and probabilistic model that attempts to imitate the writing task. Therefore, it attempts to produce a document if a topic is given. There is a variety of LDA based algorithms used in different domains, including author-topic analysis [25], LDA based bioinformatics [26], temporal text mining [27], supervised topic models, and latent co-clustering, etc. In simple words, LDA's fundamental idea is that each document is represented as a mixture of topics. Each topic represents a discrete probability distribution reflecting each word's likelihood to occur in a specific topic. Therefore, a document is described as probability distributions of words in each topic. Certainly, LDA has many applications such as role discovery [28], emotion topic [29], automatic grading of essays [30], and email filtering [31], etc. Biterm topic modeling (BTM) is a topic modeling approach over short texts. These topic modeling methods are becoming a significant job because of the pervasiveness of the short texts available on the internet. BTM is also used to discover discriminative and comprehensible latent topics from short text [32]. Recommendation system (RS) is an intelligent system that suggests items to users that might interest them. Some of the practical example applications of RSs include movie, book, tourist spot recommendations, etc. It is a point of amusement to discover how, \"People you may know\" feature on Facebook or LinkedIn. In a personalized RS, users get item suggestions based on their past behaviors and social networks-based interpersonal relationships. There are four categories of personalized recommendation systems based on the approach, content-based filtering, collaborative filtering (CF), knowledge-based filtering, and hybrid.