LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation

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.

LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation

Traditional session-based recommendation (SBR) utilizes session behavior sequences from anonymous users for recommendation. Although this strategy is highly efficient, it sacrifices the inherent semantic information of the items, making it difficult for the model to understand the true intent of the session and resulting in a lack of interpretability in the recommended results. Recently, large language models (LLMs) have flourished across various domains, offering a glimpse of hope in addressing the aforementioned challenges. Inspired by the impact of LLMs, research exploring the integration of LLMs with the Recommender system (RS) has surged like mushrooms after rain. However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR. To address the aforementioned challenges, we have proposed the LLM Integration Framework for SBR (LLM4SBR). Serving as a lightweight and plug-and-play framework, LLM4SBR adopts a two-step strategy. Firstly, we transform session data into a bimodal form of text and behavior. In the first step, leveraging the inferential capabilities of LLMs, we conduct inference on session text data from different perspectives and design the component for auxiliary enhancement. In the second step, the SBR model is trained on behavior data, aligning and averaging two modal session representations from different perspectives. Finally, we fuse session representations from different perspectives and modalities as the ultimate session representation for recommendation. We conducted experiments on two real-world datasets, and the results demonstrate that LLM4SBR significantly improves the performance of traditional SBR models and is highly lightweight and efficient, making it suitable for industrial deployment.