GraphConfRec: A Graph Neural Network-Based Conference Recommender System

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...

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Bibliographic Details
Published in2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) pp. 90 - 99
Main Authors Iana, Andreea, Paulheim, Heiko
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2021
Subjects
Data models
Graph Neural Network
Graph neural networks
Internet
Libraries
Measurement
Publishing
Recommender System
Scientific Publications
SciGraph
Semantics
Online AccessGet full text
DOI10.1109/JCDL52503.2021.00021

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Summary: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 coauthorship 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.
DOI:10.1109/JCDL52503.2021.00021