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.

提出了一种有效高效可解释的 LLM4MSR 模型，通过利用大型语言模型和层次元网络结构来提升多场景推荐的效果和可解释性。在多个数据集上验证了 LLM4MSR 的有效性、兼容性以及在工业推荐系统中的高效性并实现了更好的可解释性。