# Information Retrieval and Machine Learning Methods for Academic Expert Finding
## 4 Experimental Results and Discussion
### Results and Discussion  
In this section, we present and discuss the results of the experiments with the two document collections. Before diving into the details, it is worth pointing out that these experiments were carried out with an Intel(R) Xeon(R) Gold 6230 CPU @ 2.10 GHz with 20 cores, 314 GB of RAM, and 14 TB of space in the HDD running a Fedora 39 distribution. Table 1 presents the results obtained with the IR-based models for the PMSC-UGR collection. The results exhibit a notable degree of consistency, with only minor variations when a different number of clusters, \(K\), was used. This observation is particularly interesting, as it suggests that the choice of this parameter is not of critical importance. Furthermore, the results consistently favored K-means over LDA, although the distinctions were more pronounced for top_5 and top_10 than for top_1. When focusing on K-means, it became apparent that the inclusion of subprofiles enhanced the performance of extreme alternatives (namely, Atomic and Joint), especially for top_1 and top_5, provided that \(K\) was not excessively high. For top_10, a relatively low value for \(K\) was preferable, and in this scenario, the best outcome was achieved when using a single profile per author. Remarkably, the system demonstrated an ability to correctly identify the author or expert in more than one third of the articles, and in over two thirds of cases, the correct author was found within the first 10 recommended authors out of a pool of 9453 potential authors. Table 2 presents the results of our experiments with ML models for PMSC-UGR. It is noteworthy that some of the classical models, such as the DT, KNN and NB models, produced relatively poor results when applied to the text data. However, there was a significant improvement in their performance when we used SBERT embeddings instead of the original text, except for the DT model. On the other hand, models like SGD, LR, and MLP exhibited better results when using the raw text data. Interestingly, LR showed substantial improvements when SBERT embeddings were utilized instead of the text, while models like SGD and MLP displayed the opposite behavior. Among the deep learning models, including the CNN, LSTM and BiLSTM models, the results were consistently worse than those obtained with SGD, LR and MLP. In absolute terms, the best results were achieved by MLP using the raw text data. Unlike the results of the IR-based models, the ML-based ones exhibited considerable variability, spanning from very low to moderately good performance (see Figure 3). However, it is worth noting that even the best results achieved by the ML-based models were somewhat inferior to those of the IR-based models. \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 K-means} & \multicolumn{3}{c}{Using LDA} \\
10 & 0.3566 & 0.5874 & 0.6644 & 0.3558 & 0.5527 & 0.6316 \\
20 & 0.3565 & 0.5861 & 0.6647 & 0.3555 & 0.5585 & 0.6358 \\
54 & 0.3596 & 0.5863 & 0.6623 & 0.3563 & 0.5617 & 0.6405 \\
110 & 0.3587 & 0.5855 & 0.6587 & 0.3564 & 0.5645 & 0.6434 \\
390 & 0.3580 & 0.5812 & 0.6566 & 0.3580 & 0.5692 & 0.6454 \\ \hline Atomic & 0.3502 & 0.5706 & 0.6453 & & & \\ Joint & 0.3518 & 0.5847 & 0.6659 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Top_n results obtained for PMSC-UGR with the IR-based models.