## 1 Introduction  
The ranking of search results is one of the main challenges faced by the field of information retrieval [1; 2]. Search results are sorted so that the results best able to solve the user's need for information are ranked at the top of the page [3]. The challenges faced though are far from straightforward given that a successful ranking by relevance depends on the correct analysis and weighting of a document's properties, as well as the analysis of the need for that information and the key words used [1; 2; 4]. Relevance ranking has been successfully employed in a number of areas, including web page search engines, academic search engines, academic author rankings and the ranking of opinion leaders on social platforms [5]. Many algorithms have been proposed to automate this relevance and some of them have been successfully implemented. In so doing, different criteria are applied depending on the specific characteristics of the elements to be ordered. PageRank [6] and Hyperlink-Induced Topic Search(HITS) [7] are the best know algorithms for ranking web pages. Variants of these algorithms have also been used to rank influencers in social media, and include, for example, IP-Influence [8], TunkRank [9], TwitterRank [10] and TURank [11]. To search for academic documents, various algorithms have been proposed and used, both for the documents themselves and for their authors. These include Authority-Based Ranking [12], PopRank [13], Browsing-Based Model [14] and CiteRank [15]. All of them use the number of citations received by the articles as a search ranking factor in combination with other elements, such as publication date, the author's reputation and the network of relationships between documents, authors and affiliated institutions. Many information retrieval systems (search engines, bibliographic databases and citation databases, etc.) use relevance ranking in conjunction with other types of sorting, including chronological, alphabetical by author, number of queries and number of citations. In search engines like Google, relevance ranking is the predominant approach and is calculated by considering more than 200 factors [16; 17]. Unfortunately, Google does not release precise details about these factors, it only publishes fairly sketchy, general information. For example, the company says that inbound links and content quality are important [18; 19]. Google justifies this lack of transparency in order to fight search engine spam [20] and to prevent low quality documents from being ranked at the top of the results by falsifying their characteristics. Search engine optimization (SEO) is the discipline responsible for optimizing websites and their content to ensure they are ranked at the top of the search engine results pages (SERPs), in accordance with the relevance ranking algorithm [21]. In recent years, SEO has also been applied to academic search engines, such as Google Scholar and Microsoft Academic. This new application has received the name of \"academic SEO\" (or ASEO) [22; 23; 24; 25; 26]. ASEO helps authors and publishers to improve the visibility of their publications, thus increasing the chances that their work will be read and cited. However, it should be stressed that the relevance ranking algorithm of academic search engines differs from that of standard search engines. The ranking factors employed by the respective search engine types are not the same and, therefore, many of those used by SEO are not applicable to ASEO while some are specific to ASEO (see Table 1). SEO companies [27; 28; 29] routinely conduct reverse engineering research to measure the impact of the factors involved in Google's relevance ranking. Based on the characteristics of the pages that appear at the top of the SERPs, the factors with the greatest influence on the relevance ranking algorithm can be deduced. It is not a straightforward task since many factors have an influence and, moreover, the algorithm is subject to constant changes [30].