## 2 Related Work
### Traditional-Based Recommendation Systems  
The traditional recommendation systems comprise collaborative filtering, content-based filtering, and hybrid approaches [9]. Collaborative filtering is a widely employed approach that depends mainly on user-item interactions, covering implicit and explicit data [10]. Content-based filtering is a method that generates user profiles by considering the user's likes and experiences. Hybrid models have been developed to mitigate the limitations inherent in collaborative and content-based filtering approaches. [PERSON], [PERSON], [PERSON], and [PERSON] [11] established the term \"collaborative filtering\", which was the first recommendation system. Later, this technique was enlarged to \"recommendation systems\" to represent two basic facts: the first is that the approach may not be based on implicit participation by users, while the second refers to the fact that the method may suggest interesting items rather than filter them. The idea of collaborative filtering was intensively researched and made a big impact in the recommendation era. With its attractive usage, researchers discussed collaborative filtering from various perspectives. Additionally, with the growing popularity of recommendation algorithms, Netflix took a major step forward in October 2006 by publishing a comprehensive rating dataset. This release marked the start of a competition to encourage people to improve the recommendation model, ultimately increasing user experiences. The competition aimed to reward the individual or team that could create the most efficient and effective recommendation model with the top prize [12] available. This novel strategy accelerated breakthroughs in recommendation algorithms while also promoting a sense of community and collaboration among data scientists and machine-learning enthusiasts attempting to solve the challenging problem of personalized content recommendations. Since then, the availability of matrix factorization techniques has risen dramatically, revolutionizing the landscape of recommendation systems. Matrix factorization has become a cornerstone in the creation of sophisticated algorithmic recommendations due to its ability to detect hidden relationships and trends within user-item interaction data [13; 14]. This breakthrough has improved the accuracy of recommendations and paved the way for more personalized and effective content suggestions [15]. As a result, when consuming a wide range of products and services from movies and music to e-commerce recommendations, users have been provided with a richer, more customized experience. While several weaknesses in previous recommendation systems have been resolved, in addition to the advantages of these strategies, there are persisting issues, such as cold-start problems. In response to these persistent obstacles, for entertainment purposes, [PERSON], [PERSON], and [PERSON] [16] created a movie recommendation system that took advantage of the benefits of social networking to alleviate cold-start issues in collaborative filtering. To address this issue, the authors created a matrix of user relationships using personal information from users, such as age, gender, and employment. In addition, they employed community detection techniques that rely on edge betweenness to cluster group of users. [PERSON], [PERSON], [PERSON], and [PERSON] [17] utilized community detection techniques to strengthen the recommendation model by employing the k-clique approach. By making improvements to the k-clique method, they successfully developed an effective system for movie recommendations. Moving from collaborative to content-based filtering, one of the advantages of content-based approaches is their ability to address the user cold-start issue. While content-based approaches include non-user data-based recommendation models, they concentrate on analyzing and comprehending the intrinsic properties of items, such as movies or products, to provide suggestions.