## 3 Materials & Methods
### User Modeling  
User modeling represents all types of information related to user and user context. In this research required user information: personality, demographic data, and interest topics and similarities between users [45]. The similarities between users relied on three substantial factors: personality, demographic data, and interest topics. More specifically, it is supposed that \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) is the set of users and \(P_{u}=\{P_{O},P_{C},P_{E},P_{A},P_{N}\}\) is the big five personality trait, vector, \(D_{u}=\left\{D_{age},D_{gender}\right\}\) is the demographic data vector, and \(Z_{u}=\{z_{1},z_{2},z_{3},\ldots,z_{n}\}\) is the interest topics vectors for specific user u. To measure the similarity between users based on personality (user to user similarity), the Pearson correlation is used between the personality traits vector, as shown in the following equation:  
\[Per_{Sim(u,v)}=\frac{\sum_{i}(p_{u}^{i}-\overline{p_{u}})\left(p_{v}^{i}- \overline{p_{v}}\right)}{\sqrt{\sum_{i}\left(p_{u}^{i}-\overline{p_{u}} \right)^{2}\sum_{i}\left(p_{v}^{i}-\overline{p_{v}}\right)^{2}}} \tag{2}\]  
The notations used in this equation are explained in Table 2.  
Demographic data in terms of age and gender were used to define similarities between users based on demographics (user to user similarity). Potential gender-based preferences were considered, that is, perhaps male interests differ from females. On the other hand, the age group plays a crucial role in determining the interests, as the interests of teenagers differ from the interests of the young, and the interests of the elderly. In this case, cosine similarity is used, as demonstrated in the following equation:  
\[Dem_{Sim(u,v)}=\frac{\overset{\rightarrow}{u}\overset{\rightarrow}{\|} \overset{\rightarrow}{u}\overset{\rightarrow}{\|}\overset{\rightarrow}{\|} \overset{\rightarrow}{\|}}=\frac{\sum_{i=1}^{n}u_{i}v_{i}}{\sqrt{\sum_{i=1}^{n }u_{i}^{2}}\sqrt{\sum_{i=1}^{n}v_{i}^{2}}} \tag{3}\]  
where \(u_{i}v_{i}\) represent age and gender vector for users \(u\) and \(v\), respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(u\) & User \(u\) \\ \(v\) & User \(v\) \\ \(p_{u}\) & \(\{P_{O},P_{c},P_{E},P_{A},P_{N}\}\) \\ \(\overline{p_{u}}\) & Average value of the personality traits vector for user \(u\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notation Used in Personality Similarity. Similarity between users based on interested topics (user to user similarity), count vectorizer was applied to transform interested topics for each user, and for this case, cosine similarity was used as in demographic similarity. Our analysis was based on these three factors rather than relying on one singular aspect only, such as users' personality or demographics. In other words, the users' personalities may be similar, but their interests may differ. For example, there may be two similar users in terms of personality, where the first user is 15 years old and the second user is 50 years old; hence, they will not have the same interests despite their similarities in personality. Therefore, demographic data and personality characteristics complement each other and cannot be dispensed. Nevertheless, interest topics are the most critical factor in determining the similarity between users because they express real interests coming from the users themselves. Consequently, the final equation to find similar users can be expressed as follows:  
\[User_{Sim(u,v)}=\alpha*Per_{Sim(u,v)}+\alpha*Dem_{Sim(u,v)}+(1-\alpha)Top_{Sim (u,v)} \tag{4}\]  
where \(Per_{Sim(u,v)}\) refers to similarity based on personality, \(Dem_{Sim(u,v)}\) indicate to similarity based on demographics, \(Top_{Sim(u,v)}\) indicate similarity based on topics and \(\alpha\) is a weight parameter controls the percentage of similarity between users.