# Expert Recommendation for Answering Questions on Social Media
## 4 Performance Evaluation
When an expert was recommended by setting the expertise high in the user information, the precision was determined by verifying whether a certain generated user was recommended as an expert. In the experimental evaluations, we generated a user of the highest expertise in the dataset. We verified whether that user was recommended as an expert of the highest expertise. Subsequently, the precision was compared with those of the existing expert recommendation methods. Table 3 shows the expert recommendation results. To confirm whether the generated user was precisely recommended as an expert when the expert recommendation method was used, the expertise of a certain user was set to a high score. 100 users engaged in the SNS were used as experimental data. The information of generated user 10 was selected as an expert and set to the highest score. Because the proposed method recommended an expert by considering the interest fields, human relations, and answer quality of users, the generated user 10 was precisely shown for the ranking number 1. Meanwhile, for an existing method, i.e., the EFDT [29] method, user 10 was ranked number 7 because it did not include human relations. For the TSCFM [32] method, it ranked fourth because it did not expand the question, thereby demonstrating low accuracies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of users & 1000 \\ Number of human relations & 0–200 \\ Number of user documents & 0–100 \\ Number of keywords & 3 \\ Trust score of the document & 0–1 \\ Types of keywords & 100 \\ Answer quality score & 0–1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental data of performance evaluation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Weight** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline \(\alpha\) & 0.4 & 0.4 & 0.2 & 0.4 & 0.7 & 0.7 \\ B & 0.4 & 0.2 & 0.4 & 0.3 & 0.3 & 0 \\ \(\gamma\) & 0.2 & 0.4 & 0.4 & 0.3 & 0 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weights of user interest, human relation, and answer quality. We compared the proposed method with the existing methods in terms of recall, precision, and F-measure when the number of expert recommendations changes from 5 to 40. Recall is a proportion of users recommended as a real expert among the users classified as experts. The precision is the proportion of results classified as experts in the results recommended as experts. The F-measure is the harmonic mean of the precision and recall. Figure 8: Recall and Precision according to weight value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **Proposed Method** & **EDT** & **TSCFM** \\ \hline
1 & User 10 & User 1 & User 2 \\
2 & User 4 & User 7 & User 3 \\
3 & User 7 & User 4 & User 13 \\
4 & User 5 & User 14 & User 10 \\
5 & User 9 & User 5 & User 8 \\
6 & User 1 & User 9 & User 11 \\
7 & User 13 & User 10 & User 9 \\
8 & User 14 & User 19 & User 15 \\
9 & User 19 & User 24 & User 18 \\
10 & User 6 & User 27 & User 20 \\
11 & User 23 & User 13 & User 22 \\
12 & User 24 & User 6 & User 25 \\
13 & User 18 & User 23 & User 28 \\
14 & User 30 & User 30 & User 23 \\
15 & User 27 & User 18 & User 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Expert recommendation results. Figure 9 shows the recall according to changes in the number of expert recommendations. The proposed method showed higher recalls than the existing methods on average. All three methods indicated low precisions when five persons were recommended. In the proposed method, the recalls of more than 50% were shown when 10 persons or more were recommended. The proposed method and TSCFM exhibited higher recalls than the EFDT method. The reason was that human relations were determined in a larger scope. Furthermore, in the proposed method, since experts were recommended using the human relations, answer quality, and hierarchical structure of words in a question, higher recalls were indicated compared to the existing methods.