## 1 Introduction
In the literature, there are several different methods for building models to find experts, such as probabilistic, network-based (or sometimes called graph-based), voting, and other models [1,4]. 4. Models evaluation--the expert finding system is an IR system where interactions start with a user sending a query to the system, and then the system retrieves a list of experts that hopefully are relevant to the user's query. Normally, expert finding system efficiency is evaluated using a test collection (datasets). 5. Interaction design--the presentation of expert search results to users is a primary issue in practice. However, a simple list of names does not every time aid the user to judge the relevance of a candidate to the query. Contrary to document search, there is often no particular document snippet that can be speedily examined to determine relevance. For that reason, expert search result pages often show not only a ranked list of people but also a list of documents, conferences, and journals. Also, the presence of contact details for each ranked expert is important to facilitate communication [1]. There is a significant number of studies on locating personal homepages where several of the suggested methods utilize supervised learning techniques [31, 32, 33]. Also, photos of experts are essential because users may need to determine the likely seniority or familiarity of an expert in advance of contacting him or her. For instance, the user may be looking for somebody of a similar age or level or type of experience to themselves. Moreover, related information such as affiliations and documents, containing publications, and projects, appear to help the user determine that the expert is likely to have relevant expertise [1]. Usually, there are two important expert finding systems tasks-- (1) expert finding concentrating on identifying a particular expert in a specific domain and (2) expert profiling identifying the area of expertise of the individual experts. Earlier studies [1,4] stated that these tasks are two sides of the same coin, since, in combination, they lead to a ranking problem, wherein the collection of the objects that had to be ranked were prioritized. Despite these advances and achievements in expert finding systems research, we discovered only one survey study about expert finding systems published in 2017 [4] that analyzed expert finding systems methods and introduced data collections (datasets) used for evaluating expert finding systems. However, only six studies between 2010 and 2013 were included in the survey. Our study presents a systematic review, which includes 96 studies between 2010 and 2019. It focuses on answering five research questions represented in Section 5. In the first question, it tries to find out common domains of expert finding systems. The second question focuses on expertise sources that have been used to identify experts. The third question concentrates on the difference between expertise retrieval and seeking and theories and contextual factors that are combined into expert finding systems to improve their effectiveness. The fourth question focuses on the state-of-the-art methods that have been used for models building. Finally, the fifth question concentrates on the datasets that have been used to evaluate expert finding systems. We found gaps in efforts to identify and analyse the contextual factors and to identify the difference between expertise retrieval and seeking, as well as to identify the domains that used expert finding systems. Moreover, new methods and datasets since 2013 were not included. Hence, there is a need to generate evidence that highlights the benefits of the existing studies. This review will be very helpful for future researchers and practitioners in expert finding systems and expertise seeking domains.