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Dan Xu

SIGIR 2026×2AAAI 2026KDD 2026检索与知识对齐与偏好可解释性推理强化学习与后训练
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2 一作 · 4 篇 · SIGIR 2026 / AAAI 2026 / KDD 2026

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2026 顶会论文

4
一作SIGIR 2026Oral· Information Retrieval (Industry)

Policy-Grounded Dynamic Facet Suggestions for Job Search

Dan Xu · Baofen Zheng · Qianqi Shen · Jianqiang Shen · Wenqiong Liu · Chunnan Yao · Ping Liu · Rajat Arora · Kevin Kao · Hsiang Lin · Wanjun Jiang · Yusuke Takebuchi · Jingwei Wu · Wenjing Zhang

摘要

Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, making accurate user intent inference and relevant job retrieval particularly challenging. We present dynamic facet suggestion (DFS), an interactive...

检索与知识对齐与偏好可解释性OpenReview
一作SIGIR 2026Poster· Information Retrieval (Industry)

Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn

Dan Xu · Baofen Zheng · Jianqiang Shen · Qi Xiao · Benjamin Hoan Le · Wen Pu · Saurabh Gupta · Ran Zhou · Neha Saraf · Alice Leung · Qianqi Shen · Liangjie Hong · Jingwei Wu · Wenjing Zhang

摘要

Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job postings into standardized or derived job attributes that power numerous LinkedIn products. However, building a scalable, cost-efficient, and high-...

推理强化学习与后训练效率与系统OpenReview

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