AMENDER: An Attentive and Aggregate Multi-layered Network for Dataset Recommendation

Yujun Chen, Yuanhong Wang, Yutao Zhang, Juhua Pu, Xiangliang Zhang

Abstract: 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 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.