activity
20172021
most citedSTG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

21 citations · 56 across the 13 of their papers we have counts for

collaborators

24 papers

cs.CV20213 cited

Task Aligned Generative Meta-learning for Zero-shot Learning

Zhe Liu, Yun Li, Lina Yao +2

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer…

cs.IR20202 cited

Generative Inverse Deep Reinforcement Learning for Online Recommendation

Xiaocong Chen, Lina Yao, Aixin Sun +3

Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation…

cs.CV20201 cited

Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding

Zhe Liu, Xianzhi Wang, Lina Yao +3

Predicting consumers' purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining…

cs.LG20208 cited

Spectrum-Guided Adversarial Disparity Learning

Zhe Liu, Lina Yao, Lei Bai +2

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between…

cs.IR20208 cited

Recommender Systems for the Internet of Things: A Survey

May Altulyan, Lina Yao, Xianzhi Wang +3

Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dyna…

cs.LG2020

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

Lei Bai, Lina Yao, Can Li +2

Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an…