404 citations · 712 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Modeling Dynamic Attributes for Next Basket Recommendation
Yongjun Chen, Jia Li, Chenghao Liu +4
Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information…