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Sci-Surf: Navigating Scientific Literature Discovery through Human Feedback and Intelligent Summarization
- 基于大模型构建用户画像，动态理解研究兴趣
- 结合文本与图表生成结构化摘要，提升论文消化效率
- 实测显示推荐精准度提升10.4%，适合科研人员日常文献探索
科学论文的快速增长使研究人员难以发现并深入理解相关新成果。现有学术发现平台多依赖静态主题订阅或基于嵌入的相似性匹配，仅提供摘要或简短总结，难以支持复杂的意图建模与深度论文概括。我们提出 Sci-Surf，一种以用户意图为中心的知识发现系统，融合反馈驱动的个性化推荐与多模态博客式论文解析。该方法通过大模型生成用户画像来优化意图表示，并生成整合文本与视觉信息的结构化摘要。演示展示了一个端到端的学术发现流程，真实用户评估表明：在为期一个月的在线测试中，引入口语化用户画像后，推荐结果与实际用户偏好之间的预测一致性平均提升了10.4%。
原文摘要 · Abstract (English)
The rapid growth of scientific publications makes it increasingly difficult for researchers to identify relevant new studies and effectively comprehend them. Existing academic discovery platforms typically rely on static topic subscriptions or embedding-based similarity and provide only abstracts or short summaries, offering limited support for nuanced intent modeling and in-depth paper summarization. We present Sci-Surf, an intent-centric knowledge discovery system that integrates feedback-driven personalized recommendation with multi-modal blog-style paper digestion. Our approach refines user intent representations through LLM-based user profiling, while generating structured summaries that synthesize textual and visual information from full papers. The demo presents an end-to-end academic discovery pipeline and demonstrates measurable improvements in both recommendation quality and digestion quality through real-user evaluations. Specifically, the integration of verbalized profiles led to a 10.4% average improvement in predictive alignment with real-world user preferences throughout a month-long online evaluation.
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