A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising

• URL: http://arxiv.org/abs/2102.04033v1
• Date: Mon, 8 Feb 2021 07:11:20 GMT
• Title: A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising
• Authors: Shiyao Wang, Qi Liu, Tiezheng Ge, Defu Lian and Zhiqiang Zhang
• Abstract summary: We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. A first large-scale creative dataset, CreativeRanking, is constructed, which contains over 1.7M creatives of 500k products as well as their real impression and click data.
• Score: 31.219027299187346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creative plays a great important role in e-commerce for exhibiting products. Sellers usually create multiple creatives for comprehensive demonstrations, thus it is crucial to display the most appealing design to maximize the Click-Through Rate~(CTR). For this purpose, modern recommender systems dynamically rank creatives when a product is proposed for a user. However, this task suffers more cold-start problem than conventional products recommendation In this paper, we propose a hybrid bandit model with visual priors which first makes predictions with a visual evaluation, and then naturally evolves to focus on the specialities through the hybrid bandit model. Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. 2) Regarding visual evaluations as a prior, the hybrid bandit model (called HBM) is proposed to evolve consistently to make better posteriori estimations by taking more observations into consideration for online scenarios. 3) A first large-scale creative dataset, CreativeRanking, is constructed, which contains over 1.7M creatives of 500k products as well as their real impression and click data. Extensive experiments have also been conducted on both our dataset and public Mushroom dataset, demonstrating the effectiveness of the proposed method.

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