44 citations · 74 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model
Jianling Wang, Ainur Yessenalina, Alireza Roshan-Ghias
Online video services acquire new content on a daily basis to increase engagement, and improve the user experience. Traditional recommender systems solely rely on watch history, de…
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…
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
Ziwei Zhu, Jianling Wang, James Caverlee
Recommendation algorithms typically build models based on historical user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These…