# Information Retrieval and Machine Learning Methods for Academic Expert Finding
## 4 Experimental Results and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **top\_1** & **top\_5** & **top\_10** & **top\_1** & **top\_5** & **top\_10** \\ \hline  & & \multicolumn{3}{c}{Using text} & \multicolumn{3}{c}{Using SBERT} \\ DT & 0.0537 & 0.0613 & 0.0622 & 0.0227 & 0.0501 & 0.0703 \\ NB & 0.0665 & 0.1438 & 0.1910 & 0.1681 & 0.3641 & 0.4615 \\ KNN & 0.0683 & 0.1620 & 0.1624 & 0.2428 & 0.4703 & 0.4706 \\ RF & - & - & - & 0.1181 & 0.2344 & 0.3002 \\ SGD & 0.2272 & 0.4188 & 0.4839 & 0.1785 & 0.2648 & 0.2667 \\ LR & 0.1948 & 0.3696 & 0.4470 & 0.2707 & 0.5010 & 0.5918 \\ MLP & 0.3441 & 0.5791 & 0.6621 & 0.2365 & 0.4600 & 0.5520 \\ CNN & 0.1425 & 0.2888 & 0.3600 & & & \\ LSTM & 0.0821 & 0.2196 & 0.3111 & & & \\ BiLSTM & 0.0894 & 0.2417 & 0.3331 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top_n results obtained for PMSC-UGR with the ML-based models. Tables 3 and 4 display the results obtained from the CORD-19 collection using IR-based and ML-based models, respectively. We can observe that the obtained results partially exhibited similar trends to those of the previous experiments with PMSC-UGR while also revealing some distinct patterns. In the case of the IR-based models, the results continued to demonstrate a significant degree of similarity, even more pronounced than in the other dataset. Remarkably, neither K-means nor LDA showed superior performance compared to the other in this context. Additionally, it appears that the results slightly improved when a larger number of clusters was utilized. Among the ML-based models, the DT, NB, and KNN models continued to exhibit consistently poor results. However, it is worth noting that KNN showed significant improvement when SBERT embeddings were utilized. On the other hand, SGD, LR, and MLP demonstrated better performance. Furthermore, LR's performance still benefited from the use of SBERT embeddings, while the SGD and MLP models' performance declined in this scenario. The deep learning algorithms (CNN, LSTM, and BiLSTM) fell somewhere in \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **top\_1** & **top\_5** & **top\_10** & **top\_1** & **top\_5** & **top\_10** \\ \hline  & \multicolumn{4}{c}{Using text} & \multicolumn{4}{c}{Using SBERT} \\ DT & 0.0602 & 0.0745 & 0.0758 & 0.0093 & 0.0239 & 0.0359 \\ NB & 0.0166 & 0.0372 & 0.0504 & - & - & - \\ KNN & 0.0389 & 0.1304 & 0.1308 & 0.0835 & 0.2241 & 0.2245 \\ RF & - & - & - & 0.0363 & 0.0714 & 0.0929 \\ SGD & 0.1308 & 0.2397 & 0.2798 & 0.0614 & 0.0967 & 0.0983 \\ LR & 0.0638 & 0.1275 & 0.1659 & 0.1084 & 0.2162 & 0.2704 \\ MLP & 0.1852 & 0.3121 & 0.3696 & 0.0954 & 0.1939 & 0.2401 \\ CNN & 0.0486 & 0.1097 & 0.1495 & & & \\ LSTM & 0.0362 & 0.0927 & 0.1322 & & & \\ BiLSTM & 0.0401 & 0.1006 & 0.1411 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top_n results obtained for CORD-19 with the ML-based models. Figure 3: Top_n results for PMSC-UGR for the models based on IR using K-means (**left**) and ML using text (**right**). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(K\) & **top\_1** & **top\_5** & **top\_10** & **top\_1** & **top\_5** & **top\_10** \\ \hline  & \multicolumn{4}{c}{Using K-means} & \multicolumn{4}{c}{Using LDA} \\
10 & 0.1760 & 0.2812 & 0.3228 & 0.1717 & 0.2791 & 0.3218 \\
20 & 0.1792 & 0.2823 & 0.3244 & 0.1779 & 0.2813 & 0.3234 \\
29 & 0.1778 & 0.2834 & 0.3240 & 0.1787 & 0.2840 & 0.3248 \\
110 & 0.1861 & 0.2893 & 0.3285 & 0.1853 & 0.2922 & 0.3316 \\
239 & 0.1901 & 0.2919 & 0.3279 & 0.1883 & 0.2928 & 0.3313 \\ \hline Atomic & 0.2006 & 0.3047 & 0.3466 & & & \\ Joint & 0.1717 & 0.2874 & 0.3351 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top_n results obtained for CORD-19 with the IR-based models. between, with MLP still being the standout performer, achieving even better results than the IR-based models. It is also important to highlight that the absolute results in this case were considerably lower than those obtained with PMSC-UGR, showing an average reduction in performance of approximately 50%.