用大模型识别新闻里的隐含地点，提升本地新闻推荐效果
Beyond the Surface: Uncovering Implicit Locations with LLMs for Personalized Local News
- 用大模型分析新闻中的方言、地标等隐含线索定位区域
- 相比传统方法，大模型识别本地内容准确率更高，线上点击量提升27%
- 无需依赖知识图谱，适合大规模部署的个性化推荐系统
新闻推荐系统通过个性化主页内容提升用户参与度，但内容类型、编辑立场和地理聚焦等因素影响推荐效果。地方报纸需平衡各地区报道，但识别本地文章困难，因位置线索常以方言、地标等隐含形式出现。传统方法如命名实体识别（NER）和知识图谱可推断位置，但大语言模型（LLMs）提供了新可能，也带来准确性和可解释性挑战。本文研究了在Taboola的“为你主页”系统中使用LLMs进行本地文章分类，对比其与传统技术的表现。关键发现：(1) 知识图谱能增强NER模型对隐含位置的识别能力；(2) LLMs优于传统方法；(3) LLMs可在不依赖知识图谱的情况下有效识别本地内容。离线评估显示LLMs在隐含位置分类上表现优异，线上A/B测试表明本地内容浏览量显著增加。一个集成LLM位置分类的可扩展管道将本地文章分发量提升了27%，既保持了报纸品牌特性，又增强了主页个性化。
原文摘要 · Abstract (English)
News recommendation systems personalize homepage content to boost engagement, but factors like content type, editorial stance, and geographic focus impact recommendations. Local newspapers balance coverage across regions, yet identifying local articles is challenging due to implicit location cues like slang or landmarks. Traditional methods, such as Named Entity Recognition (NER) and Knowledge Graphs, infer locations, but Large Language Models (LLMs) offer new possibilities while raising concerns about accuracy and explainability. This paper explores LLMs for local article classification in Taboola's "Homepage For You" system, comparing them to traditional techniques. Key findings: (1) Knowledge Graphs enhance NER models' ability to detect implicit locations, (2) LLMs outperform traditional methods, and (3) LLMs can effectively identify local content without requiring Knowledge Graph integration. Offline evaluations showed LLMs excel at implicit location classification, while online A/B tests showed a significant increased in local views. A scalable pipeline integrating LLM-based location classification boosted local article distribution by 27%, preserving newspapers' brand identity and enhancing homepage personalization.
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