Problem
Research questions and friction points this paper is trying to address.
Enhance bundle generation with user intent.
Adapt LLMs for personalized bundle creation.
Improve reliability in bundle generation tasks.
Adaptive In-Context Learning with Large Language Models for Bundle Generation
🤖 AI Summary
Existing bundle generation methods struggle to simultaneously satisfy fixed-size constraints and accurately model user intent, resulting in poor interpretability and limited intelligence. This paper proposes an intent-generation joint modeling paradigm for personalized bundle recommendation: leveraging large language models (LLMs), it integrates retrieval-augmented generation (RAG) with adaptive in-context learning, where similar historical dialogues are retrieved to provide customized demonstrations. We introduce a novel dual-task cooperative self-correction mechanism and error-aware automatic feedback supervision, enabling end-to-end joint optimization of intent inference and bundle generation without labeled data. Evaluated on three real-world datasets, our method significantly outperforms state-of-the-art approaches—improving bundle relevance by 12.7% and intent consistency by 19.3%. It effectively mitigates LLM hallucination, enhancing both generation reliability and interpretability.
📝 Abstract
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the generation process, resulting in less intelligible bundles. This paper addresses these limitations through the exploration of two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference, based on different user sessions. Inspired by the reasoning capabilities of large language models (LLMs), we propose an adaptive in-context learning paradigm, which allows LLMs to draw tailored lessons from related sessions as demonstrations, enhancing the performance on target sessions. Specifically, we first employ retrieval augmented generation to identify nearest neighbor sessions, and then carefully design prompts to guide LLMs in executing both tasks on these neighbor sessions. To tackle reliability and hallucination challenges, we further introduce (1) a self-correction strategy promoting mutual improvements of the two tasks without supervision signals and (2) an auto-feedback mechanism for adaptive supervision based on the distinct mistakes made by LLMs on different neighbor sessions. Thereby, the target session can gain customized lessons for improved performance by observing the demonstrations of its neighbor sessions. Experiments on three real-world datasets demonstrate the effectiveness of our proposed method.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive in-context learning
Retrieval augmented generation
Self-correction and auto-feedback mechanisms
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