Recommending Researchers in Machine Learning based on Author-Topic Model

TL;DR

This paper uses the author-topic model to identify and visualize top researchers in machine learning over time, aiding new researchers in finding key contributors in the field.

Contribution

It introduces a method combining ATM, Hellinger distance, and t-SNE to analyze and visualize researcher similarities in machine learning.

Findings

Identified top researchers in different time periods

Mapped researcher similarities using t-SNE visualization

Provided a tool for newcomers to find leading experts

Abstract

The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top researchers and find similar researchers using their similarity score. The similarity score is calculated using Hellinger distance. The researchers are plotted using t-SNE, which reduces the dimensionality of the data while keeping the same distance between the points. The analysis of our study helps the upcoming researchers to find the top researchers in their area of interest.