# Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach
## 5 Analysis of Results
### Efficiency Evaluation  
We set up and verified scenarios in which reviewers are assigned by academic societies for evaluating the efficiency of the proposed Top-N reviewer group selection algorithm. The Korean Conference of the Korea Computer Congress (KCC 2021) published 746 of the 810 submitted papers. It took a sufficiently long time for the editor to manually select 252 reviewers. We measured the total time required to recommend the Top-N reviewer groups (N = 2-10) 1000 times with and without activating CoI prevention rules (Figure 9). Times of 1863, 1879, 1890, and 1902 s were required when recommending the Top-2, -4, -6, and -8 reviewer groups, respectively, without considering CoI. Times of 1854, 1888, 1890, and 1902 s, respectively, were required when considering CoI for the Top-2, -4, -6, and -8 reviewer groups. The experimental results showed that even when considering CoI prevention rules, there is no difference in the recommendation time. Thus, a list of reviewers can be recommended within approximately 2 s per paper when using the proposed system. The performance evaluation result of the REST API, to which the proposed algorithm is applied, processes all responses within an average of 170 s when 200 people request simultaneously; it can process 44 requests per minute (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Baseline**} & \multicolumn{3}{c}{**Our Approach**} \\ \cline{2-6}  & **Domain** & **Keyword (Y, C)** & **C-Score** & **Domain** & **Keyword (Y, C)** & **C-Score** \\ \hline \multirow{3}{*}{\(1\)} & \multirow{3}{*}{Social science} & Cloud (2016, 3), & \multirow{3}{*}{98.78} & Engineering & Cloud (2021, 2, 2020, 1), & \multirow{3}{*}{98.84} \\  & & Big Data (2016, 2), & \multirow{3}{*}{(Computer & Big Data (2021, 2, 2020, 2), & \multirow{3}{*}{98.84} \\  & (Social work) & Block Chain (2019, 2), & & & \\  & & AI(2019, 1) & & & \\ \hline \multirow{3}{*}{\(2\)} & \multirow{3}{*}{\(2\)} & Engineering & Cloud (2018, 1, 2020: 1), & \multirow{3}{*}{97.58} & \multirow{3}{*}{\begin{tabular}{c} Engineering \\ (Computer \\ Engineering) \\ \end{tabular} } & Cloud (2020, 2), & \multirow{3}{*}{98.38} \\  & (Electrical & Big Data (2020, 2), & \multirow{3}{*}{95.26} \\  & & Block Chain (2020, 1), & & \\  & & AI(2019, 2) & & & \\ \hline \multirow{3}{*}{\(3\)} & \multirow{3}{*}{\(3\)} & Medicine and & Cloud (2020: 2), & \multirow{3}{*}{95.26} \\  & pharmacy & Big Data (2015, 1), & \multirow{3}{*}{95.26} \\  & (Medicine) & Block Chain (2020, 2), & & & \\  & & AI (2020, 1) & & & \\ \hline \multirow{3}{*}{\(4\)} & Engineering & Cloud (2020: 2), & \multirow{3}{*}{72.56} & \multirow{3}{*}{\begin{tabular}{c} Engineering \\ (Computer \\ Engineering) \\ \end{tabular} } & Cloud (2020: 2), & \multirow{3}{*}{92.56} \\  & (Computer & Big Data (2020, 2), & \multirow{3}{*}{92.56} \\  & AI (2020, 2) & & & \\ \hline \multirow{3}{*}{\(5\)} & Social science & \multirow{3}{*}{\(5\)} & \multirow{3}{*}{\begin{tabular}{c} Big Data (2020, 1), \\ AI (2020, 2, 2019, 1) \\ \end{tabular} } & \multirow{3}{*}{58.79} & \multirow{3}{*}{\begin{tabular}{c} Engineering \\ (Computer \\ Engineering) \\ \end{tabular} } & Cloud (2019, 1), & \multirow{3}{*}{96.07} \\  & (Computer & Big Data (2017, 2), & \multirow{3}{*}{92.56} \\  & AI (2020, 2) & & & \\ \hline \multirow{3}{*}{\(5\)} & Social science & \multirow{3}{*}{\(5\)} & \multirow{3}{*}{\
\begin{tabular}{c} Big Data (2020, 1), \\ AI (2020, 2, 2019, 1) \\ \end{tabular} } & \multirow{3}{*}{58.79} & \multirow{3}{*}{
\begin{tabular}{c} Engineering \\ (Computer \\ Engineering) \\ \end{tabular} } & Cloud (2018, 3), & \multirow{3}{*}{96.81} \\  & (Business administration) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the baseline and our approach.