Qitian Wu

Seq2Bubbles

2021

User behavior sequences contain rich information about user interests and are exploited to predict user's future clicking in sequential recommendation. Existing approaches, especially recently proposed deep learning models, often embed a sequence of clicked items …

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

2019

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of …