## 1 Introduction
One of the disadvantages of relying on the expressed interests of users during the registration process is that, oftentimes, the user is not able to provide accurate information because they may not know the answers, do not want to talk about them, or fill the responses in quickly to access the provided service. Moreover, users interest their expressed during the registration cannot automatically adjust to shifts and changing in users' interests. User interest prediction methods that auto-discover users' preferences for active social media topics have emerged as a growing research area due to their potential to enhance the effectiveness of applications such as retweet prediction [4] and news recommendations [5]. The majority of current user interest identification research focuses on inferring explicit interests based on the written data provided by users [6; 7]. However, most social network users are inactive, and the content available to them is limited and does not reveal enough information about their interests; this is known as the sparsity problem. In contrast, new users do not offer historical information or any content and, therefore, personalised recommendations cannot be made for them; this is known as the cold start problem. Nonetheless, many researchers still try to solve these two problems by modelling user interests and focusing on inferring users' implicit interests (has explanation in Appendix A) [8; 9]. Many researchers have introduced a graph-based relation prediction system that integrates user and topic parameters into a single representation model to examine the appropriateness of users' social interactions and topic connectedness in implicit interest identification [10; 11]. However, user modeling (has explanation in Appendix A) and mining users' interests face many problems that affect the efficiency and reduce the quality of the system. In addition, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, user modeling refers to capturing specific aspects of a particular user. It can also express it in the user's actual representation, through which it can explore the user's interests. For example, there is a user on Twitter, so the tweets' text represents the central part of modeling this user. Moreover, most research focuses on one fact of representing the user's interests and neglecting other significant facts that precisely influence and support user modeling, such as the user's personality and demographic data (has explanation in Appendix A). Based on user modeling's various dimensions, users' multiple aspects and views are considered, instead of focusing on user content only. Multi-faceted user interest profiles (has explanation in Appendix A) should be given further consideration, as prior research has shown that they are more effective than a single facet [12]. Secondly, relevant and related factors in inferring users' interests include the significant relationships between users and the relationships between topics. Moreover, studying the interaction patterns between the users and the topics helps to infer the users' implicit interests. On the other hand, the researchers provided a hybrid diagram-based model to represent the topics and users. However, the field still requires further investigation and possesses some limitations, e.g., whether the user's interests are meaningful, or whether they are linked together cooperatively. In other words, if the user holds an explicit interest (has explanation in Appendix A) in a topic, one can infer that the user may also be interested in topics linguistically or collaboratively related to their explicit interests [13; 14]. Thirdly, providing a recommendation or customizing a tremendous amount of information based on users' interests still suffers from a cold-start problem due to the lack of information available to new users. Researchers have presented solutions to this problem; however, it still requires further investigation [13; 14; 15].