6 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation
Chengzhi Lin, Shuchang Liu, Chuyuan Wang +1
Accurately predicting watch time is crucial for optimizing recommendations and user experience in short video platforms. However, existing methods that estimate a single average wa…
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning
Yifan Du, Hangyu Guo, Kun Zhou +6
Visual instruction tuning is crucial for enhancing the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). In this paper, we aim to investigate a fund…
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Chenrui Zhang, Lin Liu, Jinpeng Wang +4
As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To furt…