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。

LLM4MSR: 多场景推荐的增强 LLM 模型

LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation

Multi-scenario recommendation is dedicated to retrieve relevant items for
users in multiple scenarios, which is ubiquitous in industrial recommendation
systems. These scenarios enjoy portions of overlaps in users and items, while
the distribution of different scenarios is different. The key point of
multi-scenario modeling is to efficiently maximize the use of whole-scenario
information and granularly generate adaptive representations both for users and
items among multiple scenarios. we summarize three practical challenges which
are not well solved for multi-scenario modeling: (1) Lacking of fine-grained
and decoupled information transfer controls among multiple scenarios. (2)
Insufficient exploitation of entire space samples. (3) Item's multi-scenario
representation disentanglement problem. In this paper, we propose a
Scenario-Adaptive and Self-Supervised (SASS) model to solve the three
challenges mentioned above. Specifically, we design a Multi-Layer Scenario
Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select
and fuse effective transfer information from whole scenario to individual
scenario in a quite fine-grained and decoupled way. To sufficiently exploit the
power of entire space samples, a two-stage training process including
pre-training and fine-tune is introduced. The pre-training stage is based on a
scenario-supervised contrastive learning task with the training samples drawn
from labeled and unlabeled data spaces. The model is created symmetrically both
in user side and item side, so that we can get distinguishing representations
of items in different scenarios. Extensive experimental results on public and
industrial datasets demonstrate the superiority of the SASS model over
state-of-the-art methods. This model also achieves more than 8.0% improvement
on Average Watching Time Per User in online A/B tests.

本研究提出一种场景自适应与自监督模型 (SASS)，解决具有多场景问题的细粒度信息转移、全空间样本利用和物品多场景表示分离问题，并在公共和工业数据集上得到显著提升。