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 Doc2Vec 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.

Keywords

• scholar recommendation
• multi-layer perceptron
• attention mechanism
• textual similarity
• social relevance
• personal contribution rates