Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation: for given users, the goal is to generate personalized bundle creatives that the users will be interested in. To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. Experiments on large-scale real-world datasets verify that our proposed model shows significant advantages in terms of creative quality and generation speed.
@article{arxiv.2205.14970,
title = {Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding},
author = {Penghui Wei and Shaoguo Liu and Xuanhua Yang and Liang Wang and Bo Zheng},
journal= {arXiv preprint arXiv:2205.14970},
year = {2022}
}