发布时间：2026-08-21

文章标题：联合指导本科生论文被EMNLP2026主会录用

摘要：论文题目：PGPO: Prototype-Guided Progressive Obfuscation for Privacy-Preserving LLM-Enhanced Recommendation 论文作者：Shengxiang Lin, Jiajie Su, Pengyang Zhou, Xiang Chen, Xiaolin Zheng, Feng Tian, Chaochao Chen

内容：

录用通知如下：

Dear Authors,

Congratulations! We are delighted to inform you that your submission to EMNLP 2026, PGPO: Prototype-Guided Progressive Obfuscation for Privacy-Preserving LLM-Enhanced Recommendation, has been accepted as a Main Conference paper. We look forward to welcoming you to EMNLP 2026 in Budapest. Please carefully review the camera-ready requirements and information below.

This year, we received an unprecedented 17669 submissions. We accepted 2719 as Main Conference papers and 2533 to Findings of the ACL. This represents an acceptance rate of 15.4% for Main Conference papers and 14.3% for Findings papers.

Abstract: LLM-enhanced recommender systems encode item side information into semantic embeddings, but these embeddings may leak sensitive textual attributes through inversion attacks. Existing input-side defenses rely on trusted collection or local token perturbation, often disrupting the inter-item semantic topology needed for recommendation. To address it, we introduce relational fidelity as the key objective, requiring semantic shift for privacy while preserving relative item relations for utility. Based on this principle, we propose prototype-guided progressive obfuscation (PGPO), which learns topology-aware obfuscation anchors for high-influence prototype words and propagates them through a static semantic graph. PGPO is optimized with GRPO using individual dissimilarity, structural consistency, and semantic constraint rewards. Experiments on two benchmarks show strong inversion defense with near-upper-bound recommendation performance. The code is available at https://anonymous.4open.science/r/PGPO/.