3 papers
cs.IR2025
Stratified Expert Cloning for Retention-Aware Recommendation at Scale
Chengzhi Lin, Annan Xie, Shuchang Liu +3
User retention is critical in large-scale recommender systems, significantly influencing online platforms' long-term success. Existing methods typically focus on short-term engagem…
cs.IR2025
AlignPxtr: Aligning Predicted Behavior Distributions for Bias-Free Video Recommendations
Chengzhi Lin, Chuyuan Wang, Annan Xie +5
In video recommendation systems, user behaviors such as watch time, likes, and follows are commonly used to infer user interest. However, these behaviors are influenced by various…
cs.IR2024
Dreaming User Multimodal Representation Guided by The Platonic Representation Hypothesis for Micro-Video Recommendation
Chengzhi Lin, Hezheng Lin, Shuchang Liu +5
The proliferation of online micro-video platforms has underscored the necessity for advanced recommender systems to mitigate information overload and deliver tailored content. Desp…