Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks
Date
2021
Authors
Chen, X.
Tang, T.
Ren, J.
Lee, I.
Chen, H.
Xia, F.
Editors
Gao, X.
Huang, G.
Cao, J.
Cao, J.
Deng, K.
Huang, G.
Cao, J.
Cao, J.
Deng, K.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
ACM International Conference Proceeding Series, 2021 / Gao, X., Huang, G., Cao, J., Cao, J., Deng, K. (ed./s), pp.29-36
Statement of Responsibility
Conference Name
IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (14 Dec 2021 - 17 Dec 2021 : AUSTRALIA, Melbourne)
Abstract
With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions.
We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths.
With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach.
School/Discipline
Dissertation Note
Provenance
Description
Link to a related website: https://unpaywall.org/10.1145/3498851.3498926, Open Access via Unpaywall
Access Status
Rights
Copyright 2021 Copyright held by the owner/author(s)