# Personalized Scholar Recommendation Based on Multi-Dimensional Features
###### Abstract  
The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation), which improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2 Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-\(N\) scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the \(N\) value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect. s _applied_  
Received: 20 August 2021  
Accepted: 15 September 2021  
17 September 2021  
**Published:** **17 September 2021**  
acteristics with item characteristics and use different recommendation algorithms to recommend the required information to users by analyzing the correlation between the characteristics [5]. Cognitive recommendation systems will be a new type of intelligent recommendation systems, which can realize intelligent operation in complex and constantly changing environments [6]. Nowadays, recommendation systems are widely used in search engines, e-commerce, social platforms, etc. [7; 8; 9]. Especially during COVID-19, these systems open a new mode of online scholar cooperation and academic exchanges, and more human activities have moved to the online world; thus, it is particularly important to provide scholars with a suitable cooperation platform and communication environment. Most existing work on scholar recommendation focuses on public academic platforms (e.g., question answering communities, DBLP dataset, etc.) [10]. However, not all platforms provide publicly available datasets, e.g., the scholar recommendation on the ResearchGate platform is relatively weak. In this paper, we propose a novel personalized scholar recommendation approach, abbreviated as Mul-RSR (Multi-dimensional features based Research Scholar Recommendation). Specifically, the main contributions of this paper are as follows:  
* We design a personalized scholar recommendation approach. We fully mine the behavioral information of scholars. Personal features are defined from user behavior to quantify the correlation between scholars. These features include text features (i.e., text information of academic papers), social features (i.e., social relationship information among scholars) and academic features (i.e., academic behavior information such as the number of citations). * The multi-layer perceptron (MLP) and attention mechanisms are used to learn the input of feature data, and the Doc2 Vec text representation model and the random walk algorithm are respectively used to calculate the textual similarity and the social relationship between scientific scholars. Thus, the feature information of each dimension is fully mined. * We provide a ResearchGate dataset crawled by the Selenium crawler tool. We conduct a series of experiments based on public and self-collected datasets. The experimental results demonstrate Mul-RSR's superior performance on recommendation compared with other recommendation methods. The rest of the paper is organized as follows: Section 2 reviews the work relevant to recommendations on traditional and academic social platforms.