AMENDER: An attentive and aggregate multi-layered network for dataset recommendation
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Permanent link to this recordhttp://hdl.handle.net/10754/661888
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AbstractIn this paper, we study the problem of recommending the appropriate datasets for authors, which is implemented to infer the proximity between authors and datasets by leveraging the information from a three-layered network, composed by authors, papers and datasets. To link author-dataset semantically by taking advantage of the rich content information of papers in the intermediate layer, we design an attentive and aggregate multi-layer network learning model. The aggregation is for integrating the intra-layer information of paper content and citations, while the attention is used for coordinating authors at the top-layer and datasets at the bottom-layer in the semantic space learned from papers in the intermediate layer. The experimental study demonstrates the superiority of our method compared with the solutions that extend existing models to our problem.
CitationChen, Y., Wang, Y., Zhang, Y., Pu, J., & Zhang, X. (2019). AMENDER: An Attentive and Aggregate Multi-layered Network for Dataset Recommendation. 2019 IEEE International Conference on Data Mining (ICDM). doi:10.1109/icdm.2019.00112
SponsorsThe authors would like to thank the anonymous reviewers for their helpful comments. This work is supported by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, National Key Research and Development Program of China (2017YFB1002000), Science Technology and Innovation Commission of Shenzhen Municipality (JCYJ20180307123659504), and the State Key Laboratory of Software Development Environment in Beihang University.
Conference/Event name19th IEEE International Conference on Data Mining, ICDM 2019