Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatly limits the practical application of LLM4Rec because (i) LLMs are costly to train and infer frequently, and (ii) LLMs struggle to access real-time data (its large number of parameters poses an obstacle to deployment on devices). Fortunately, small recommendation models (SRMs) can effectively supplement these shortcomings of LLM4Rec diagrams by consuming minimal resources for frequent training and inference, and by conveniently accessing real-time data on devices. In light of this, we designed the Device-Cloud LLM-SRM Collaborative Recommendation Framework (LSC4Rec) under a device-cloud collaboration setting. LSC4Rec aims to integrate the advantages of both LLMs and SRMs, as well as the benefits of cloud and edge computing, achieving a complementary synergy. We enhance the practicability of LSC4Rec by designing three strategies: collaborative training, collaborative inference, and intelligent request. During training, LLM generates candidate lists to enhance the ranking ability of SRM in collaborative scenarios and enables SRM to update adaptively to capture real-time user interests. During inference, LLM and SRM are deployed on the cloud and on the device, respectively. LLM generates candidate lists and initial ranking results based on user behavior, and SRM get reranking results based on the candidate list, with final results integrating both LLM's and SRM's scores. The device determines whether a new candidate list is needed by comparing the consistency of the LLM's and SRM's sorted lists. Our comprehensive and extensive experimental analysis validates the effectiveness of each strategy in LSC4Rec.

翻译：基于大型语言模型的推荐系统（LLM4Rec）是一个前景广阔的研究方向，在该领域已展现出卓越性能。然而，其无法捕捉实时用户偏好的缺陷极大地限制了LLM4Rec的实际应用，原因在于：（i）大型语言模型的训练与频繁推理成本高昂；（ii）大型语言模型难以访问实时数据（其庞大的参数量成为设备端部署的障碍）。幸运的是，小型推荐模型（SRMs）能够有效弥补LLM4Rec的上述不足：它们能以极少的资源消耗进行频繁训练与推理，并可便捷地访问设备端实时数据。基于此，我们在设备-云端协同框架下设计了设备-云端LLM-SRM协同推荐框架（LSC4Rec）。LSC4Rec旨在整合大型语言模型与小型推荐模型的优势，以及云计算与边缘计算的益处，实现互补协同效应。我们通过设计三种策略来增强LSC4Rec的实用性：协同训练、协同推理与智能请求。在训练阶段，大型语言模型生成候选列表以增强小型推荐模型在协同场景下的排序能力，并使小型推荐模型能够自适应更新以捕捉实时用户兴趣。在推理阶段，大型语言模型与小型推荐模型分别部署于云端与设备端。大型语言模型基于用户行为生成候选列表与初始排序结果，小型推荐模型基于候选列表进行重排序，最终结果融合两者的评分。设备端通过比较大型语言模型与小型推荐模型排序列表的一致性来决定是否需要新的候选列表。我们全面而广泛的实验分析验证了LSC4Rec中各项策略的有效性。