## 2 Related Studies
### Expert Recommendation Systems  
Many researchers use search engines to locate an expert to work with, yet in various scenarios, they do not receive optimal outcomes during the initial phases because of their keyword-based searches. Several recommendation approaches have been proposed in order to rank the authors based on their expertise in a specific area [21; 22; 23; 24].  
[PERSON] et al. [25] proposed a novel technique for the analysis and extraction of relevant experts by integrating a topic model and a paper cooperation network. Additionally, with the help of latent Dirichlet allocation (LDA), expert cooperation networks play a vital role in the extraction process of expert topics; for instance, if two experts often publish their research work together, they are likely to be associated with the same field. The experimental evaluation indicates that the perplexity of the model decreased first and then increased when they expanded the number of topics. The generalizability of the model was also achieved with the implementation of an optimal number of topics.  
Furthermore, [PERSON] et al. [26] proposed a contextualized embedding method (document-centric) for expertise data extraction rather than non-contextual techniques (word or team-centric), which investigated the effects of retrofitting on contextualized embeddings. In addition, they compared two different ways of merging paper titles and abstracts by embedding what was used to extract the semantic similarity. They also tested four innovative strategies to rank the authors of co-authored papers. Today, most papers are written by multiple authors; therefore, the ranking systems used in this study for scoring authors based on the strength of their connection to their paper's topic was a novel idea. Meanwhile, ref. [27] focused on finding experts in the field of computer science, specifically with regard to faculties of computer science at Indonesian universities, based on the abstracts of respective scholars' theses from students at Fasilkom. The model was designed based on word and vector representation by incorporating doc2 vec and word2 vec. Thus, suitable experts were identified to supervise theses subject to their expertise by using the cosine similarity to assign them. However, the results showed that using word2 vec to represent the expertise performs was more productive than using doc2 vec.  
[PERSON] et al. [28] proposed a network approach for expertise retrieval using credit allocation for co-authors through analyses of the degree of each co-author's contribution to collaborative work, and it assigns expertise level according to their contribution. Thereby, the proposed methodology worked on the base of credit allocation, specific author papers, and medical subject heading (MeSH) connections using a topic-modeling LDA method. The weighted version of HeteAlloc uncovered the concentration of authors on a particular topic and classified the papers with smaller MeSh terms. The unweighted version is very helpful when the range of topics is too widely spread and most papers have multiple MeSh terms. Nevertheless, the unweighted version is often employed because the MEDLINE dataset mostly carries publications with numerous MeSh terms. Moreover, [PERSON] [29] proposed the expert finding system (EFS) where the quality of the results for a team of expert researchers will outperform the results for individual work. To guage the similarity, they used two measurements: co-occurrence and shared neighbor. As a result, the product value of the two weights gives an estimate that can indicate the relationship between the expertise of an author and the actual work of the user. Consequently, it provides a list of names for collaboration with an expert in a specific field, with an accuracy rate of 71.50%.