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.

With the advances in technology and smart devices, more and more attention has been paid to model spatial correlations, temporal dynamics, and friendship influence over point-of-interest (POI) checkins. Besides directly capturing general user's checkin behavior, existing works mostly highlight the intrinsic feature of POIs, i.e., spatial and temporal dependency. Among them, the family of methods based on Markov chain can capture the instance-level interaction between a pair of POI checkins, while recurrent neural network (RNN) based approaches (state-of-the-art) can deal with flexible length of checkin sequence. However, the former is not good at capturing high-order POI transition dependency, and the latter cannot distinguish the exact contribution of each POI in a historical checkin sequence. Moreover, in recurrent neural networks, local and global information is propagated along the sequence through one bottleneck i.e., hidden states only.In this work, we design a novel model to enforce contextual constraints on sequential data by designing a spatial and temporal attention mechanisms over recurrent neural network that leverages the importance of POIs visited by users in given time interval and geographical distance in successive checkins. Attention mechanism helps us to learn which POIs bounded by time difference and spatial distance in user checkin history are important for the prediction of next POI. Moreover, we also consider periodicity and friendship influence in our model design. Experimental results on two real location based social networks Gowalla, and BrightKite show that our proposed method outperforms the existing state-of-the-art deep neural network methods for next POI prediction and understanding user transition behavior. We also analyze the sensitivity of parameters including context window for capturing sequential effect, temporal context window for estimating temporal attention and spatial context window for estimating spatial attention respectively.