AMENDER: An Attentive and Aggregate Multilayered Network for Dataset Recommendation In 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 threelayered network, composed by authors, papers and datasets. To link authordataset semantically by taking advantage of the rich content information of papers in the intermediate layer, we design an attentive and aggregate multilayer network learning model. The aggregation is for integrating the intralayer information of paper content and citations, while the attention is used for coordinating authors at the toplayer and datasets at the bottomlayer 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.

Dataset Recommendation via Variational Graph Autoencoder This paper targets on designing a querybased dataset recommendation system, which accepts a query denoting a userxe2x80x99s research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the userxe2x80x99s 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 twolayer 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 twolayer 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.