103 citations · 430 across the 38 of their papers we have counts for
9 papers · 1 filter
Session-based Recommendation with Hypergraph Attention Networks
Jianling Wang, Kaize Ding, Ziwei Zhu +1
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms. A critical challenge is to accurately model user in…
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo +1
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommenda…
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Kaize Ding, Jianling Wang, James Caverlee +1
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance…
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
Kaize Ding, Dingcheng Li, Alexander Hanbo Li +4
Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on larg…
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
Jianling Wang, Kaize Ding, James Caverlee
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, howev…
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in mo…