# Information Retrieval and Machine Learning Methods for Academic Expert Finding
## 1 Introduction  
In both our personal and professional lives, there are moments when our knowledge or ability to solve a problem falls short, necessitating us to connect with individuals whose experience or expertise can provide valuable assistance for the issue at hand. If a tool were available for such situations, it could assist us in locating these individuals, who may be represented within a system through various means, such as the services they offer (in structured or unstructured formats), CVs, blog entries, tags, and more. These matches would typically be identified based on our expressed needs, either through a natural language query or a simple list of keywords. This software would be responsible for determining the most suitable experts who can fulfill our requirements. This problem is called expert finding and in general refers to the process of identifying individuals who possess specialized knowledge or expertise in a particular subject or field. The goal is to locate individuals who can provide valuable insight, guidance, or assistance in relation to a specific topic. Expert finding has found success in various applications, including in relation to enterprises [1, 2], community question answering [3, 4], social networks [5, 6], online forums [7], medicine [8], and academia [9, 10, 11, 12]. A strong indicator of the significance of and interest in expert finding is the abundance of publications on the topic, including a substantial number of survey papers [13, 14, 15, 16, 17, 18, 19]. We focus on the academic or scholarly field, where the search for experts typically involves identifying the key figures (researchers, scholars, professors) who have pioneered fundamental concepts, made significant advancements, or possess in-depth knowledge and expertise in specific academic disciplines. Two illustrative use cases that exemplify the process of finding academic experts are as follows: (1) An international project office seeks to identify reviewers for evaluating project proposals submitted for a particular call. In this scenario, the office administrators aim to identify academics whose research expertise aligns with the proposal topics, as expressed through keywords, so they can carry out the evaluation task. (2) A multidisciplinary research team is engaged in a project spanning multiple knowledge domains, necessitating the inclusion of researchers from various fields. In such cases, the research team leaders strive to identify other research groups or specific researchers who excel in fields that make them essential for incorporation in their research. Taking it to the next level, the goal of this paper is to study academic-expert-finding methods by utilizing information retrieval (IR) and machine learning (ML), including classical and deep learning models. In this case, the academics are represented by the papers published in journals or conferences, giving clear evidence of what their expertise fields are. Then, the recommendation of experts is carried out by providing a text where potential users express their information needs. These are typically represented by a kind of abstract describing the problem and the type of required knowledge. This might be seen as a recommendation process, where a hypothetical system recommends experts by means of ranking them, given an information need. The IR-based methods construct multifaceted textual profiles (subprofiles) for each expert by clustering information from their scientific publications. An IR system (IRS) indexes these subprofiles and retrieves those most similar to the query expressing the required expertise. In this paper, we present a set of IR-based alternatives fully adapted to the problem. ML-based methods treat the problem as a classification task. They use the publications authored by each expert to train an automatic text classifier, where the categories represent different experts.