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ICDM2019_DatasetRecommendation (1).pdf
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Accepted manuscript
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Date
2019Permanent link to this record
http://hdl.handle.net/10754/661922
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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.Citation
Altaf, B., Akujuobi, U., Yu, L., & Zhang, X. (2019). Dataset Recommendation via Variational Graph Autoencoder. 2019 IEEE International Conference on Data Mining (ICDM). doi:10.1109/icdm.2019.00011Sponsors
This work is supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.Conference/Event name
2019 IEEE International Conference on Data Mining (ICDM)Additional Links
https://ieeexplore.ieee.org/document/8970775/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8970775
ae974a485f413a2113503eed53cd6c53
10.1109/ICDM.2019.00011