most citedUnconstrained Monotonic Calibration of Predictions in Deep Ranking Systems

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2026

UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization

Jialei Li, Yang Zhang, Yimeng Bai +7

Generative recommendation has recently emerged as a transformative paradigm that directly generates target items, surpassing traditional cascaded approaches. It typically involves…

cs.IR2025

Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization

Suyu Chen, Yimeng Bai, Yulong Huang +2

Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own…

cs.IR2025

DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation

Chang Liu, Yimeng Bai, Xiaoyan Zhao +3

Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods…

cs.IR20252 cited

Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems

Yimeng Bai, Shunyu Zhang, Yang Zhang +5

Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate abs…

cs.CL2025

Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization

Yilun Qiu, Xiaoyan Zhao, Yang Zhang +5

Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personaliza…