Dataset Recommendation via Variational Graph Autoencoder

Basmah Altaf, Uchenna Thankgod Akujuobi, Lu Yu, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations


This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user's research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user's research need. The motivation of building such a system is to save users from spending time on heavy literature review work to find usable datasets.We start by constructing a two-layer network: one layer of citation network, and the other layer of datasets, connected to the firstlayer papers in which they were used. A query highlights a set of papers in the citation layer. However, answering the query as a naive retrieval of datasets linked with these highlighted papers excludes other semantically relevant datasets, which widely exist several hops away from the queried papers. We propose to learn representations of research papers and datasets in the two-layer network using heterogeneous variational graph autoencoder, and then compute the relevance of the query to the dataset candidates based on the learned representations. Our ranked datasets shown in extensive evaluation results are validated to be more truly relevant than those obtained by naive retrieval methods and adoptions of existing related solutions.
Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Data Mining (ICDM)
Number of pages10
ISBN (Print)9781728146041
StatePublished - 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.


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