As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve their recommendation performance. However, existing methods tend to integrate insufficient scenario knowledge and neglect learning personalized cross-scenario preferences, thus leading to suboptimal performance and inadequate interpretability. Meanwhile, though large language model (LLM) has shown great capability of reasoning and capturing semantic information, the high inference latency and high computation cost of tuning hinder its implementation in industrial recommender systems. To fill these gaps, we propose an effective efficient interpretable LLM-enhanced paradigm LLM4MSR in this work. Specifically, we first leverage LLM to uncover multi-level knowledge including scenario correlations and users' cross-scenario interests from the designed scenario- and user-level prompt without fine-tuning the LLM, then adopt hierarchical meta networks to generate multi-level meta layers to explicitly improves the scenario-aware and personalized recommendation capability. Our experiments on KuaiSAR-small, KuaiSAR, and Amazon datasets validate two significant advantages of LLM4MSR: (i) the effectiveness and compatibility with different multi-scenario backbone models (achieving 1.5%, 1%, and 40% AUC improvement on three datasets), (ii) high efficiency and deployability on industrial recommender systems, and (iii) improved interpretability. The implemented code and data is available to ease reproduction.

翻译：随着对更个性化推荐需求的增长以及商业场景的急剧繁荣，多场景推荐（MSR）的研究备受关注，它利用所有场景的数据来同时提升其推荐性能。然而，现有方法往往整合的场景知识不足，且忽视了学习个性化的跨场景偏好，从而导致次优的性能和不足的可解释性。同时，尽管大语言模型（LLM）已展现出强大的推理和语义信息捕获能力，但其较高的推理延迟和调优计算成本阻碍了其在工业推荐系统中的实施。为填补这些空白，我们在本工作中提出了一种高效、可解释的LLM增强范式LLM4MSR。具体而言，我们首先利用LLM，通过设计的场景级和用户级提示（无需对LLM进行微调）来揭示多层级知识，包括场景关联和用户的跨场景兴趣；随后，采用分层元网络来生成多层级元层，以显式提升场景感知和个性化推荐能力。我们在KuaiSAR-small、KuaiSAR和Amazon数据集上的实验验证了LLM4MSR的两个显著优势：（i）有效性和与不同多场景骨干模型的兼容性（在三个数据集上分别实现了1.5%、1%和40%的AUC提升），（ii）在工业推荐系统中的高效率和可部署性，以及（iii）增强的可解释性。已公开实现的代码和数据以便复现。