GraphConfRec: A Graph Neural Network-Based Conference Recommender System

TL;DR

GraphConfRec is a novel conference recommendation system that leverages graph neural networks and scholarly relationships to improve venue suggestions, aiding researchers in selecting suitable conferences.

Contribution

It introduces a graph neural network-based approach combining co-authorship, citation, and content data for conference recommendation, which is a novel integration in this context.

Findings

Recall@10 up to 0.580

MAP up to 0.336

User study with 25 subjects supports effectiveness

Abstract

In today's academic publishing model, especially in Computer Science, conferences commonly constitute the main platforms for releasing the latest peer-reviewed advancements in their respective fields. However, choosing a suitable academic venue for publishing one's research can represent a challenging task considering the plethora of available conferences, particularly for those at the start of their academic careers, or for those seeking to publish outside of their usual domain. In this paper, we propose GraphConfRec, a conference recommender system which combines SciGraph and graph neural networks, to infer suggestions based not only on title and abstract, but also on co-authorship and citation relationships. GraphConfRec achieves a recall@10 of up to 0.580 and a MAP of up to 0.336 with a graph attention network-based recommendation model. A user study with 25 subjects supports the positive results.