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arXiv · 2205.14970

Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding

Penghui Wei·Shaoguo Liu·Xuanhua Yang·Liang Wang·Bo Zheng
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Abstract

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.

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Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang, Bo Zheng. 2022-06-09. Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding. https://arxiv.org/abs/2205.14970

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