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.

Related papers

• CTR-Driven Advertising Image Generation with Multimodal Large Language Models [53.40005544344148]
  We explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL) Our method achieves state-of-the-art performance in both online and offline metrics.
  arXiv  Detail & Related papers  (2025-02-05T09:06:02Z)
• A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model [8.945197427679924]
  Traditional AI-based approaches face the same problem of not considering user information while having limited aesthetic knowledge from designers. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. This paper proposes a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage.
  arXiv  Detail & Related papers  (2024-01-17T03:27:39Z)
• AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting [7.515971669919419]
  In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. We introduce the task of generative models for creative generation that incorporate user interests, and itshape AdBooster, a model for personalized ad creatives.
  arXiv  Detail & Related papers  (2023-09-08T12:57:05Z)
• Retrieval-Enhanced Contrastive Vision-Text Models [61.783728119255365]
  We propose to equip vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks.
  arXiv  Detail & Related papers  (2023-06-12T15:52:02Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• A Black-Box Attack Model for Visually-Aware Recommender Systems [7.226144684379191]
  Visually-aware recommender systems (RS) have recently attracted increased research interest. In this work, we show that relying on external sources can make an RS vulnerable to attacks. We show how a new visual attack model can effectively influence the item scores and rankings in a black-box approach.
  arXiv  Detail & Related papers  (2020-11-05T08:43:12Z)
• Pre-training Graph Transformer with Multimodal Side Information for Recommendation [82.4194024706817]
  We propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction.
  arXiv  Detail & Related papers  (2020-10-23T10:30:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.