# Information Retrieval and Machine Learning Methods for Academic Expert Finding
## 5 Conclusions  
In this paper, we addressed the challenge of academic expert finding: i.e., the task of searching for researchers who are experts in a particular scientific field. We approached this problem using two distinct methods: information retrieval and machine learning. Our objective was to compare these techniques from these two major areas and determine if one was more suitable than the other in terms of performance for this problem. On one hand, IR-based techniques considered all the published papers of the authors as a collection of documents. These documents were used to feed an IRS, which generated a ranking of authors based on the provided information needs. More specifically, IR methods were grounded in the concept of profile, which acted as a \"container\" for storing an author's papers, thereby reflecting their expertise. We explored three types of profiles, ranging from the simplest approaches to more sophisticates ones: (1) a single profile containing all the text from an author's published papers; (2) treating each paper as an individual subprofile, resulting in a collection of subprofiles within the overall profile; and (3) different subprofiles based on distinct clusters/topics mined from the whole dataset. To achieve this, we employed methods such as clustering and LDA to extract and capture the underlying topics from the entire document collection and build subprofiles for each author with papers from the same topic, trying to acquire the different topics in which the author was an expert. On the other hand, ML-based techniques treated this problem as a classification task. In this approach, publications and their associated words served as features, while their corresponding authors were treated as classes. In this study, we conducted an extensive evaluation of classifiers spanning from classical methods to the latest advancements in deep learning. To the best of our knowledge, there is a noticeable absence of prior comprehensive experimental studies, despite the apparent potential, applying ML and deep learning techniques to the task of expert finding. This fact is configured as one of the contributions of this paper. To assess the effectiveness of the various techniques employed in this study, we subjected them to testing with two distinct collections of scientific papers: PMSC-UGR and CORD-19. The former comprises published articles in the wider field of biomedicine, while the latter focuses more specifically on heterogeneous aspects related to COVID-19. These datasets also exhibit disparities in size, including the numbers of papers and authors, as well as variations in the homogeneity of topics. When considering the results obtained during the evaluation phase, it is important to emphasize that IR techniques were well suited for this problem, consistently delivering strong performance. They possessed a significant advantage in that they yielded more consistent results across various techniques, making their application less reliant on parameter-tuning (e.g., the number of clusters/topics used) and computational resources. In contrast, ML methods generally exhibited lower performance, with the exception of MLP, which achieved results similar to those of IR-based techniques but required careful parameter optimization. When we delve into the insights garnered from IR-based techniques, specifically those centered around topicality, such as clustering using K-means and LDA, we find that they performed similarly to the more basic Joint and Atomic models, albeit with marginal differences. However, their application could prove beneficial in real-world search scenarios where the information derived from clusters can provide added value by offering explanations for recommendations. For instance, in a study such as the one mentioned in [78] where an evaluation with real users was conducted, tag clouds from various subprofiles played a pivotal role as explanatory tools for the recommender system's outputs.