English(EN) Generative Long-term User Interest Modeling for Click-Through Rate Prediction

新的GenLI模型通过兴趣生成增强点击率预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-15 12:42

研究人员开发了一个名为GenLI的新模型，以改进广告和推荐系统中的点击率（CTR）预测。GenLI通过生成多样化的、与目标无关的用户兴趣分布，解决了现有两阶段框架的局限性。这种方法避免了复杂、耗时的匹配过程，并整合了用户行为之间的交互，以实现更准确、更高效的预测。 AI

影响引入了一种新颖的生成模型，以提高广告和推荐系统中点击率预测的准确性和效率。

排序理由该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 108 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Xingxing Wang · 2026-05-15 12:42

面向点击率预测的生成式长期用户兴趣建模

Modeling long-term user interests with massive historical user behaviors enhances click-through rate (CTR) prediction performance in advertising and recommendation systems. Typically, a two-stage framework is widely adopted, where a general search unit (GSU) first retrieves top-$…