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    Dataset Recommendation via Variational Graph Autoencoder

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    Name:
    ICDM2019_DatasetRecommendation (1).pdf
    Size:
    1.336Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Altaf, Basmah cc
    Akujuobi, Uchenna Thankgod cc
    Yu, Lu
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/661922
    
    Metadata
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    Abstract
    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.2019.00011
    Sponsors
    This work is supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2019 IEEE International Conference on Data Mining (ICDM)
    DOI
    10.1109/ICDM.2019.00011
    Additional Links
    https://ieeexplore.ieee.org/document/8970775/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8970775
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDM.2019.00011
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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