GraphConfRec: A Graph Neural Network-Based Conference Recommender System

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.

arXiv commentAccepted at the Joint Conference on Digital Libraries (JCDL 2021)

References in corpus (7)

• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Graph Neural Networks: A Review of Methods and Applications
• Graph Transformer Networks
• Deep Learning on Graphs: A Survey
• A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
• Advancing GraphSAGE with A Data-Driven Node Sampling