404 citations · 712 across the 15 of their papers we have counts for
9 papers · 1 filter
Generating Negative Samples for Sequential Recommendation
Yongjun Chen, Jia Li, Zhiwei Liu +4
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervisio…
MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
Wenzhuo Yang, Jia Li, Caiming Xiong +1
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods oft…
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
ELECRec: Training Sequential Recommenders as Discriminators
Yongjun Chen, Jia Li, Caiming Xiong
Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical inte…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…
ConTinTin: Continual Learning from Task Instructions
Wenpeng Yin, Jia Li, Caiming Xiong
The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to so…
Styleverse: Towards Identity Stylization across Heterogeneous Domains
Jia Li, Jie Cao, JunXian Duan +1
We propose a new challenging task namely IDentity Stylization (IDS) across heterogeneous domains. IDS focuses on stylizing the content identity, rather than completely swapping it…
Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li +2
Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often…
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li +3
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender s…