The personalized bundle generation problem, which aims to create a preferred
bundle for user from numerous candidate items, receives increasing attention in
recommendation. However, existing works ignore the order-invariant nature of
the bundle and adopt sequential modeling methods as the solution, which might
introduce inductive bias and cause a large latency in prediction. To address
this problem, we propose to perform the bundle generation via
non-autoregressive mechanism and design a novel encoder-decoder framework named
BundleNAT, which can effectively output the targeted bundle in one-shot without
relying on any inherent order. In detail, instead of learning sequential
dependency, we propose to adopt pre-training techniques and graph neural
network to fully embed user-based preference and item-based compatibility
information, and use a self-attention based encoder to further extract global
dependency pattern. We then design a permutation-equivariant decoding
architecture that is able to directly output the desired bundle in a one-shot
manner. Experiments on three real-world datasets from Youshu and Netease show
the proposed BundleNAT significantly outperforms the current state-of-the-art
methods in average by up to 35.92%, 10.97% and 23.67% absolute improvements in
Precision, Precision+, and Recall, respectively.

本文介绍了一种基于非自回归机制的个性化捆绑生成方法，采用了预训练技术和图神经网络来提取用户偏好和物品兼容性信息，并使用自注意力机制的编码器来提取全局依赖模式，进而设计了一种置换等变的解码架构，能够直接一次性输出所需的捆绑，实验结果表明，所提出的 BundleNAT 在精度、增强精度和召回率上相较于当前最先进方法显著提高了 35.92％、10.97％和 23.67％。

非自回归个性化捆绑生成

Non-autoregressive Personalized Bundle Generation

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.

利用大型语言模型的逻辑推理能力，本文探索个性化捆绑生成和基于用户交互的意图推断这两个相关的任务，通过引入动态上下文学习范式，使 ChatGPT 能从相关会话中的示范中获取量身定制和动态的教训，并在目标会话中执行任务。实验结果在三个真实数据集上证实了我们方法在这两个任务上的有效性。此外，推断的意图对于其他有趣的下游任务，如设计吸引人的捆绑名称，也能发挥作用。