activity
20192026
most citedGeneralizing to the Future: Mitigating Entity Bias in Fake News Detection

85 citations · 176 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning

Huishi Luo, Shuokai Li, Hanchen Yang +10

Awakening dormant users, who remain engaged but exhibit low conversion, is a pivotal driver for incremental GMV growth in large-scale e-commerce platforms. However, existing approa…

cs.IR2022

Customized Conversational Recommender Systems

Shuokai Li, Yongchun Zhu, Ruobing Xie +5

Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-m…

cs.IR2022★ 62 cited

User-Centric Conversational Recommendation with Multi-Aspect User Modeling

Shuokai Li, Ruobing Xie, Yongchun Zhu +3

Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understandi…

cs.CL2022★ 85 cited

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection

Yongchun Zhu, Qiang Sheng, Juan Cao +3

The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the…

cs.LG2019★ 29 cited

Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

Feiyang Pan, Shuokai Li, Xiang Ao +2

Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representation…