Multi-interest modeling in current recommender systems (RS) is mainly based on user behavioral data, capturing user interest preferences from multiple dimensions. However, since behavioral data is implicit and often highly sparse, it is challenging to understand users' complex and diverse interests. Recent studies have shown that the rich semantic information in the text can effectively supplement the deficiencies of behavioral data. Despite this, it is still difficult for small models to directly extract semantic features associated with users' deep interests. That is, how to effectively align semantics with behavioral information to form a more comprehensive and accurate understanding of user interests has become a critical research problem.To address this, we propose an LLM-assisted explicit and implicit multi-interest learning framework (named EIMF) to model user interests on two levels: behavior and semantics. The framework consists of two parts: Implicit Behavioral Interest Module (IBIM) and Explicit Semantic Interest Module (ESIM). The traditional multi-interest RS model in IBIM can learn users' implicit behavioral interests from interactions with items. In ESIM, we first adopt a clustering algorithm to select typical samples and design a prompting strategy on LLM to obtain explicit semantic interests. Furthermore, in the training phase, the semantic interests of typical samples can enhance the representation learning of behavioral interests based on the multi-task learning on semantic prediction and modality alignment. Therefore, in the inference stage, accurate recommendations can be achieved with only the user's behavioral data. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed EIMF framework, which effectively and efficiently combines small models with LLM to improve the accuracy of multi-interest modeling.

翻译：当前推荐系统中的多兴趣建模主要基于用户行为数据，从多个维度捕捉用户的兴趣偏好。然而，由于行为数据是隐式的且通常高度稀疏，理解用户复杂多样的兴趣具有挑战性。近期研究表明，文本中丰富的语义信息能够有效补充行为数据的不足。尽管如此，小型模型仍难以直接提取与用户深层兴趣相关的语义特征。也就是说，如何有效对齐语义与行为信息，以形成更全面、更准确的用户兴趣理解，已成为一个关键的研究问题。为此，我们提出了一种LLM辅助的显式与隐式多兴趣学习框架（命名为EIMF），从行为和语义两个层面建模用户兴趣。该框架包含两部分：隐式行为兴趣模块（IBIM）和显式语义兴趣模块（ESIM）。IBIM中的传统多兴趣推荐系统模型可以从用户与物品的交互中学习用户的隐式行为兴趣。在ESIM中，我们首先采用聚类算法选择典型样本，并设计LLM提示策略以获取显式语义兴趣。此外，在训练阶段，基于语义预测和多模态对齐的多任务学习，典型样本的语义兴趣能够增强行为兴趣的表征学习。因此，在推理阶段，仅使用用户行为数据即可实现精准推荐。在真实数据集上的大量实验证明了所提出的EIMF框架的有效性，该框架高效地将小型模型与LLM结合，提升了多兴趣建模的准确性。