Product bundling has evolved into a crucial marketing strategy in e-commerce. However, current studies are limited to generating (1) fixed-size or single bundles, and most importantly, (2) bundles that do not reflect consistent user intents, thus being less intelligible or useful to users. This paper explores two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference based on users' interactions in a session, leveraging the logical reasoning capability of large language models. We introduce a dynamic in-context learning paradigm, which enables ChatGPT to seek tailored and dynamic lessons from closely related sessions as demonstrations while performing tasks in the target session. Specifically, it first harnesses retrieval augmented generation to identify nearest neighbor sessions for each target session. Then, proper prompts are designed to guide ChatGPT to perform the two tasks on neighbor sessions. To enhance reliability and mitigate the hallucination issue, we develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision based on the distinct mistakes made by ChatGPT on various neighbor sessions. Thus, the target session can receive customized and dynamic lessons for improved performance by observing the demonstrations of its neighbor sessions. Finally, experimental results on three real-world datasets verify the effectiveness of our methods on both tasks. Additionally, the inferred intents can prove beneficial for other intriguing downstream tasks, such as crafting appealing bundle names.

翻译：摘要：产品捆绑已发展成为电子商务中至关重要的营销策略。然而，当前研究局限于生成（1）固定规模或单一捆绑包，更重要的是（2）无法反映一致用户意图的捆绑包，因此对用户而言可理解性或实用性较低。本文利用大语言模型的逻辑推理能力，探索两项相互关联的任务：个性化捆绑生成及其基于会话中用户交互的潜在意图推断。我们提出一种动态上下文学习范式，使ChatGPT能够在执行目标会话任务时，从紧密相关的会话中获取定制化、动态的示范知识。具体而言，该方法首先利用检索增强生成技术为每个目标会话识别最近邻会话，随后设计恰当的提示词引导ChatGPT对邻居会话执行上述两项任务。为增强可靠性并缓解幻觉问题，我们开发了：（1）一种自纠正策略，在无监督信号下促进两项任务的相互改进；（2）一种自动反馈机制，基于ChatGPT在不同邻居会话上产生的独特错误，循环提供动态监督。通过观察邻居会话的示范，目标会话可接收定制化、动态的经验以提升性能。最后，在三个真实数据集上的实验结果验证了该方法在两项任务上的有效性。此外，推断出的意图还可为其他有趣的下游任务（如创作吸引人的捆绑包名称）提供有益支持。