推荐系统如何从大语言模型中收益：【①一定程度上解决冷启动的问题（训练数据不够时，可以使用LLM，当数据足够时，可以使用现有小模型）】【In-Context-Learning】【有Zeroshot能力】

小灰灰 2024-02-21 08:37 11阅读 0赞

## 一、LLM，Yet Another Solution to RecSys? ##

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推荐系统旨在解决信息过载问题来满足用户的个性化需求。

大语言模型凭借其强大的涌现能力在各个应用领域都取得了经验的表现。

大语言模型能够为推荐系统带来充分的语义信号与外部知识；但是缺乏协同信号，推理代价高；

大语言模型更适合对话式的推荐；

传统推荐系统与大模型结合的方式：

*  传统推荐系统作为BackBone，大模型作为辅助；（更有效）
    
     *  大模型对用户的理解更到位；
     *  传统推荐系统更多使用用户ID，物品ID作为特征输入，无法捕捉用户的意图；所以大语言模型更适合对话式的推荐；
 *  大模型作为BackBone，传统推荐系统作为plugin；

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## 二、Uncovering ChatGPT’s Capabilities in Recommender Systems ##

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### 不训练大模型的表现 ###

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### 训练小模型后的比较结果 ###

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可以看出，大模型如果不经过fine-tuning，效果不如经过数据训练后的小模型；  
另一个方面，如果训练数据不足的话，大模型还是可以一定程度解决冷启动问题；  
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大模型在不进行任何fine-tuning的情况下，是不具备uers-item的交互信息；  
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通过Fine-tuning来让LLM具备某一个domain的结构化推荐知识；

## 三、大模型推荐技术及展望 ##

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[大模型与推荐论坛][Link 1]

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[Link 1]: https://www.datafuntalk.com/detail/l_64d341c5e4b007b201af6e99/4