LARR：基于大语言模型辅助并具备语义理解的实时场景推荐
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
AI总结：
针对传统推荐系统缺乏实时场景语义理解及LLM处理长文本效率低的问题，提出LARR框架，通过注入领域知识并利用聚合编码器整合LLM输出，高效提升基于LLM的CTR建模与推荐性能。
摘要：
点击率（CTR）预测对推荐系统（RS）至关重要，旨在为用户提供外卖、电商等众多方面的个性化推荐服务。然而，传统RS依赖协同信号，缺乏对实时场景的语义理解。我们还注意到，将大语言模型（LLM）用于实际推荐目的的一个主要挑战是其在处理长文本输入时的效率。为突破上述问题，我们提出了大语言模型辅助实时场景推荐（LARR），采用LLM进行语义理解，在RS中利用实时场景信息，而无需LLM直接处理整个实时场景文本，从而提升基于LLM的CTR建模效率。具体而言，将推荐领域特定知识注入LLM，随后RS采用聚合编码器从LLM的独立输出中构建实时场景信息。首先，借助特殊标记，在由推荐数据构建的语料库上对LLM进行持续预训练。随后，通过对比学习在三种样本构建策略上对LLM进行微调。通过此步骤，LLM被转化为文本嵌入模型。最后，由编码器聚合LLM针对不同场景特征的独立输出，与RS中的协同信号对齐，增强推荐模型的性能。
英文摘要：
Click-Through Rate (CTR) prediction is crucial for Recommendation System(RS), aiming to provide personalized recommendation services for users in many aspects such as food delivery, e-commerce and so on. However, traditional RS relies on collaborative signals, which lacks semantic understanding to real-time scenes. We also noticed that a major challenge in utilizing Large Language Models (LLMs) for practical recommendation purposes is their efficiency in dealing with long text input. To break through the problems above, we propose Large Language Model Aided Real-time Scene Recommendation(LARR), adopt LLMs for semantic understanding, utilizing real-time scene information in RS without requiring LLM to process the entire real-time scene text directly, thereby enhancing the efficiency of LLM-based CTR modeling. Specifically, recommendation domain-specific knowledge is injected into LLM and then RS employs an aggregation encoder to build real-time scene information from separate LLM's outputs. Firstly, a LLM is continual pretrained on corpus built from recommendation data with the aid of special tokens. Subsequently, the LLM is fine-tuned via contrastive learning on three kinds of sample construction strategies. Through this step, LLM is transformed into a text embedding model. Finally, LLM's separate outputs for different scene features are aggregated by an encoder, aligning to collaborative signals in RS, enhancing the performance of recommendation model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
