## 4 Experiment and Results
### Proposed Similarity Measure Experiment  
This section introduces the experiments that were performed to find similarities between users. Four experiments were conducted at this phase. The first experiment examined the similarity between users based on the big five personality traits. The following step sought out inter-user likenesses from a demography perspective. In contrast, the third study explored the similarity between users based on each user's interests. The last experiment studied the similarities based on the enhanced equation, including the three previously identified factors: big five personality traits, demographic data, and interested topics. During the first experiment, the similarity between users was calculated based on the big five personality traits. The similarity between users based on personality was calculated using Pearson correlation similarity. The similarity matrix between sample of six users shown in Table 6.  
The second experiment calculated the similarity between users based on the demographic data. The demographic data in this experiment were age and gender. Data transformation was applied to these attributes to replace them with a numerical value. For example, the male is one, and the female is zero. The similarity between users based on demographic data was calculated using cosine similarity. The resulting similarity matrix for previous sample shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Users** & **User 1** & **User 2** & **User 3** & **User 4** & **User 5** & **User 6** \\ \hline User 1 & 1.000000 & 0.989397 & 0.98959 & 0.798963 & 0.995085 & 0.368327 \\ User 2 & 0.989397 & 1.000000 & 0.989676 & 0.801039 & 0.976672 & 0.349782 \\ User 3 & 0.98959 & 0.989676 & 1.000000 & 0.861633 & 0.970762 & 0.451998 \\ User 4 & 0.798963 & 0.801039 & 0.861633 & 1.000000 & 0.753588 & 0.834950 \\ User 5 & 0.995085 & 0.976672 & 0.970762 & 0.753588 & 1.000000 & 0.320103 \\ User 6 & 0.368327 & 0.349782 & 0.451998 & 0.83495 & 0.320103 & 1.000000 \\ Max (for 100 users) & & 1.00000 & & & & \\ Min (for 100 users) & & \(-\)0.27906 & & & & \\ Mean(for 100 users) & & 0.823224 & & & & \\ IQR & 0.016541 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Similarity Matrix based on Personality. Figure 10: Optimal k Value based on Elbow Method. The third avenue for exploration considered topical similarities between users. The similarity data was calculated using cosine similarity. The resulting similarity matrix sample is shown in Table 8.  
The final experiment calculated the similarity between users based on three essential factors, namely the big five personality traits, demographic data, and topics of interest. Based on these three factors, the similarity between users was calculated using both Pearson correlation and cosine similarity. The resulting similarity matrix sample is shown in Table 9.  
During the topic similarity experiment, the Gensim FastText model was applied based on word to vector embedding. The Gensim FastText model was trained with a skip-gram training algorithm. Moreover, Word vector dimensions were set up with a 60-window size. Furthermore, the worker was set up as 12, which indicates multicore machines that will allow for faster preparation. The result of this experiment was semantic similarity between topics. For example, the semantics between the game and videogame topics were 85%, which indicated a more