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.

Multigraph Convolution Collaborative Filtering Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a useru0027s preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from useritem interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolutionbased recommendation framework, named MultiGraph Convolution Collaborative Filtering (MultiGCCF), which explicitly incorporates multiple graphs in the embedding learning process. MultiGCCF not only expressively models the highorder information via a bipartite useritem interaction graph, but integrates the proximal information by building and processing useruser and itemitem graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several stateoftheart collaborative filtering and graph neural networkbased recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.