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English(EN) Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

LLM个性化研究探索推荐与实时适应

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探讨了个性化大型语言模型（LLM）的不同方法。第一篇论文侧重于推荐系统，比较了用于生成语义ID（SIDs）的自回归束搜索与多令牌预测，发现尽管成本更高，但束搜索在产品关键指标上表现更好。第二篇论文通过使用用户的成对偏好来实时适应LLM，解决了在线个性化问题，并提出了一种在辅导案例研究中显著降低遗憾的方法。 AI

影响 这些论文引入了定制LLM输出的新技术，有望改善推荐系统和交互式应用中的用户体验。

排序理由 arXiv上发表的两篇学术论文，详细介绍了LLM个性化的新颖方法。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

LLM个性化研究探索推荐与实时适应

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arXiv上发表的两篇学术论文，详细介绍了LLM个性化的新颖方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Sejoon Oh, Fernando Amat Gil, Evan Smothers, Dan Iter, Mark Thornburg, Dawit Mureja Argaw, Sourabh Medapati, Thea Wang, Ria Verma, Qiuling Xu, Harshit Syal, Ehsan Gholami, Moumita Bhattacharya, Ashish Rastogi ·

    基于语义ID的个性化LLM推荐：自回归束搜索 vs. 多Token预测

    arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a short sequence of discrete hierarchical content cod…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ashish Rastogi ·

    基于语义ID的个性化大语言模型推荐：自回归束搜索与多Token预测的比较

    Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a short sequence of discrete hierarchical content codes. A single instruction-tuned model can then unif…

  3. arXiv stat.ML TIER_1 English(EN) · Romain Puech, Georgy Salakhutdinov, Dimitris Bertsimas ·

    跨用户配对偏好在线LLM个性化

    arXiv:2610.17203v1 Announce Type: cross Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We study inference-time personalization through online selection from a fixed set …

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