Quoc Viet Hung Nguyen

TL;DR: A novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way is proposed and the experimental results show superiority of the proposed PME model in terms of prediction accuracy and scalability.

TL;DR: This work proposes a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation that consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short- term preference learning.

TL;DR: This article proposes a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotmporal contexts for large-scale POI recommendation and demonstrates that the STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.

TL;DR: In this paper, AIR is proposed, namely attentional intention-aware recommender systems to predict category-wise future user intention and collectively exploit the rich heterogeneous user interaction behaviors to capture varied effect of different types of actions for recommendation.

TL;DR: AugCF is a generic and effective CF model called AugCF that supports a wide variety of recommendation tasks and is based on Conditional Generative Adversarial Nets that additionally consider the class as a feature to generate new interaction data, which can be a sufficiently real augmentation to the original dataset.