  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.
