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Product bundling has evolved into a crucial marketing strategy in e-commerce.However, current studies are limited to generating (1) fixed-size or singlebundles, and most importantly, (2) bundles that do not reflect consistent userintents, thus being less intelligible or useful to users. This paper explorestwo interrelated tasks, i.e., personalized bundle generation and the underlyingintent inference based on users' interactions in a session, leveraging thelogical reasoning capability of large language models. We introduce a dynamicin-context learning paradigm, which enables ChatGPT to seek tailored anddynamic lessons from closely related sessions as demonstrations whileperforming tasks in the target session. Specifically, it first harnessesretrieval augmented generation to identify nearest neighbor sessions for eachtarget session. Then, proper prompts are designed to guide ChatGPT to performthe two tasks on neighbor sessions. To enhance reliability and mitigate thehallucination issue, we develop (1) a self-correction strategy to foster mutualimprovement in both tasks without supervision signals; and (2) an auto-feedbackmechanism to recurrently offer dynamic supervision based on the distinctmistakes made by ChatGPT on various neighbor sessions. Thus, the target sessioncan receive customized and dynamic lessons for improved performance byobserving the demonstrations of its neighbor sessions. Finally, experimentalresults on three real-world datasets verify the effectiveness of our methods onboth tasks. Additionally, the inferred intents can prove beneficial for otherintriguing downstream tasks, such as crafting appealing bundle names.

Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation