GraphConfRec: A Graph Neural Network-Based Conference Recommender System
This addresses the challenge for researchers, especially early-career ones, in selecting appropriate conferences from many options, though it is incremental as it applies existing graph neural network methods to a specific domain.
The paper tackles the problem of recommending suitable academic conferences for researchers by proposing GraphConfRec, a system that uses graph neural networks to incorporate title, abstract, co-authorship, and citation data, achieving a recall@10 of up to 0.580 and a MAP of up to 0.336.
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.