# Information Retrieval and Machine Learning Methods for Academic Expert Finding
## 2 Related Work  
Within academia, in addition to works where the goal is to find experts for a general, unspecified task [11; 22; 23], there are also works focusing on more specific tasks, such as supervisor identification [9] or reviewer recommendation for journals, conferences [12; 24; 25; 26; 27], or even grant proposals [28]. Another application involves searching for collaborators in a research field, which may entail seeking collaboration within the user's own research area or in a different field. These two situations may necessitate different methods. In the first case, when identifying experts with similar expertise, we can directly compute similarities between the target researcher and potential candidates [10; 29]. In the second case, we need to identify candidates who match the terms or topics that describe the desired expertise [30; 31]. In our case, we focus on general methods where the input to the system is text expressing the required knowledge. Approaches to expert finding can typically be categorized as content-based models, link structure-based models (also known as network-based models), or hybrid models that combine elements of both [32]. Content-based models rely on textual content from documents associated with each expert for information [33; 34; 35]. In contrast, link structure-based models leverage scholarly networks based on co-authorship or co-citation relationships [36; 37; 38; 39], often employing algorithms like PageRank, HITS, or random walk with restart to analyze these relationships. Since our models are content-based, our focus is on this type of approach. Content-based models can utilize textual content directly, considering terms from documents. They often integrate all documents' text into a single personal profile or analyze each document separately [34; 40; 41; 42]. In the first case, these models combine all the documents associated with each expert, merging their text into a single document that represents the expert's profile. This approach generates a unified profile for each expert that encompasses their expertise as demonstrated through their own documents. This method is called the single-document author model in [43] and also referred to as a candidate-centric or query-independent approach [44]. In the second case, each document associated with an expert is treated as an individual unit, forming a subprofile that focuses on the specific expertise related to the content of that document. With this method, an expert will have multiple subprofiles corresponding to the number of associated documents they have. This is known as a document-based method [33] and is also called a query-dependent approach [34]. If a single profile were constructed from all the documents of the experts, it would amalgamate all their topics of interest. This could lead to more general topics overshadowing specialized ones, resulting in an inaccurate representation of the expert's interests. Consequently, there is a risk that they might not be identified when searching for specific expertise. Conversely, the document-based method excessively atomizes the information about an expert's expertise by assigning one subprofile for each document, as many of the associated documents can be related to the same expertise. This extreme atomization could compromise the comprehensibility of the subprofiles. Regarding the efficacy of these two methods, while document-based approaches are generally deemed superior to profile-based methods [33], certain studies have arrived at the opposite conclusion [41; 45]. Therefore, it may be a matter of the specific application being considered. Our IR-based models adopt an intermediate approach by generating multiple subprofiles for each expert using clustering methods, aiming to ensure that each subprofile represents a specific topic or a homogeneous group of topics.