103 citations · 330 across the 70 of their papers we have counts for
7 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…
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…
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…
Identifying Hijacked Reviews
Monika Daryani, James Caverlee
Fake reviews and review manipulation are growing problems on online marketplaces globally. Review Hijacking is a new review manipulation tactic in which unethical sellers "hijack"…
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…
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka +8
In recent years, significant effort has been invested verifying the reproducibility and robustness of research claims in social and behavioral sciences (SBS), much of which has inv…
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…