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％。