# Expert Recommendation for Answering Questions on Social Media
## 4 Performance Evaluation  
The superiority of the proposed method is verified through the performance comparison between the proposed expert recommendation method and the existing methods. First, a performance evaluation is conducted using the existing method developed by [29], which recommends an expert by analyzing the activity information of users. After generating dynamic profiles by collecting and analyzing the recent activities of each user, the method developed by [29] generates a question cluster using the ontology when a question is requested. Subsequently, by comparing the expanded question and the dynamic profiles of users, it recommends the most similar user as an expert. Herein, the method developed by [29] is defined as the Expert Finding considering Dynamic Profiles and Trust (EFDT). The second existing method is TSCFM [32], which analyzes the social activities of users on SNS and recommends a user suitable for the question and context by considering the user's human relations, location, and time. Since these two existing methods recommend an expert by analyzing the social activities of users, we choose them for the performance comparison. The experimental evaluation was conducted in an environment of Intel Core i5 4440 @ 3.10 GHz, 8.00 GB Dual-Channel DDR3, and Windows 7 Ultimate K 64-bit. The analysis was performed for the expert recommendation results of using the proposed expert recommendation method. Moreover, an investigation was performed regarding how the recommendation result changed and the problems that occurred when the weights of user interest, human relation, and answer quality changed in the proposed method. For the experimental data, arbitrary user data were generated with normal distribution because the amount of significant data was small in the actual data. The experimental data are shown in Table 1. The number of users was 1000; the number of human relations of the user ranged from 0 to 200; the number of documents left by the user ranged from 0 to 100; the number of keywords extracted from a document was 3; the trust score of a document ranged from 0 to 1 point; the number keyword types was 100; the answer quality ranged  
Figure 6: Procedure of expert recommendation. Figure 7: Example of matching a user question with expert groups. from 0 to 1 point. For each user, the numbers were generated randomly to satisfy the normal distribution. Additionally, the Hannanum morphological analyzer [33] developed by KAIST was used to extract keywords. The proposed method cannot satisfy every user because the importance of the desired attribute is different for each user with respect to the interest fields, human relations, and answer quality of users. Moreover, it is difficult to set one overall weight as an important attribute. Therefore, in this paper, a different value is assigned to each weight to conduct the experimental evaluation and the recommendation results, owing to the weight value change changes. Table 2 shows the different values of weights \(\alpha\), \(\beta\), \(\gamma\) used in Equation (9). The weight of \(\alpha\) is the user interest, the weight of \(\beta\) is the human relation, and the weight of \(\gamma\) is the answer quality. When the number of the recommended experts is 20 among 100 persons, we measure the recall and precision by changing the weight values. Figure 8 shows the recall and precision of expert recommendation results based on the weight values in Table 2. As a result of performing experimental evaluations using various weights, it was shown that the recall and precision were the highest when \(\alpha\) = 0.4, \(\beta\) = 0.4, and \(\gamma\) = 0.2. It was shown that the probability of obtaining excellent performance in the expert recommendation result is high when a high weight is assigned to the user interest. Furthermore, the human relation demonstrated a higher probability of recommending an accurate expert compared with the answer quality of an expert.