paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
CK

Changsung Kang

SIGIR 2026×2检索与知识对齐与偏好效率与系统多模态
新星分贡献者
40
一作贡献0
核心/参与17
方向广度8
合作网络3
0 一作 · 2 篇 · SIGIR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
作者位次 #2/3SIGIR 2026Poster· Information Retrieval (Industry)

Learning to Summarize for Search Relevance with Reinforcement Learning

Nitin Yadav · Changsung Kang · Hongwei Shang

摘要

E-commerce search ranking models face the challenging and critical problem of balancing strict real-time latency constraints with the need for high-quality relevance predictions. In production environments, ranking models often rely primarily on product titles, which frequently omit critical attr...

效率与系统检索与知识对齐与偏好OpenReview
作者位次 #4/8SIGIR 2026Poster· Information Retrieval (Industry)

BERT-Based Cross-Encoder for Large-Scale Engagement Prediction and Re-ranking in Walmart Search Engine

Shuyi Chen · Philip Fu · Ajit Puthenputhussery · Changsung Kang · Ming Sun · Cun Mu · Sachin Yadav · Hongwei Shang

摘要

Product search systems must not only return relevant items but also understand users' implicit preferences beyond their explicit queries. For instance, when searching for "steak", most users implicitly prefer beef steak over equally relevant alternatives like pork steak. Predicting such engagemen...

检索与知识对齐与偏好多模态OpenReview

常见合作者