"You eat with your eyes first": Optimizing Yelp Image Advertising

• URL: http://arxiv.org/abs/2011.01434v1
• Date: Tue, 3 Nov 2020 02:49:40 GMT
• Title: "You eat with your eyes first": Optimizing Yelp Image Advertising
• Authors: Gaurab Banerjee, Samuel Spinner, Yasmine Mitchell
• Abstract summary: We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. We achieve 90-98% accuracy in classifying simplified star ratings for various image categories and observe that images containing blue skies, open surroundings, and many windows are correlated with higher Yelp reviews.
• Score: 0.8594140167290099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A business's online, photographic representation can play a crucial role in its success or failure. We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. After preprocessing the Yelp dataset, we use transfer learning to train a classifier which accepts Yelp images and predicts star-ratings. Additionally, we then train a GAN to qualitatively investigate the common properties of highly effective images. We achieve 90-98% accuracy in classifying simplified star ratings for various image categories and observe that images containing blue skies, open surroundings, and many windows are correlated with higher Yelp reviews.

Related papers

• Ranking-aware adapter for text-driven image ordering with CLIP [76.80965830448781]
  We propose an effective yet efficient approach that reframes the CLIP model into a learning-to-rank task. Our approach incorporates learnable prompts to adapt to new instructions for ranking purposes. Our ranking-aware adapter consistently outperforms fine-tuned CLIPs on various tasks.
  arXiv  Detail & Related papers  (2024-12-09T18:51:05Z)
• AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling [11.996211235559866]
  Image Content Appeal Assessment (ICAA) is a novel metric that quantifies the level of positive interest an image's content generates for viewers. ICAA is different from traditional Image-Aesthetics Assessment (IAA), which judges an image's artistic quality.
  arXiv  Detail & Related papers  (2024-07-08T01:40:32Z)
• Product Review Image Ranking for Fashion E-commerce [0.0]
  We train our network to rank bad-quality images lower than high-quality ones. Our proposed method outperforms the baseline models on two metrics, namely correlation coefficient, and accuracy, by substantial margins.
  arXiv  Detail & Related papers  (2023-08-10T07:09:13Z)
• Auditing Yelp's Business Ranking and Review Recommendation Through the Lens of Fairness [10.550608462255056]
  This study investigates the bias of Yelp's business ranking and review recommendation system. We find that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants' location in hotspot regions and their average exposure.
  arXiv  Detail & Related papers  (2023-08-04T04:12:33Z)
• VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining [53.470662123170555]
  We propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset.
  arXiv  Detail & Related papers  (2023-03-24T23:57:28Z)
• Saliency Guided Contrastive Learning on Scene Images [71.07412958621052]
  We leverage the saliency map derived from the model's output during learning to highlight discriminative regions and guide the whole contrastive learning. Our method significantly improves the performance of self-supervised learning on scene images by +1.1, +4.3, +2.2 Top1 accuracy in ImageNet linear evaluation, Semi-supervised learning with 1% and 10% ImageNet labels, respectively.
  arXiv  Detail & Related papers  (2023-02-22T15:54:07Z)
• Reviews in motion: a large scale, longitudinal study of review recommendations on Yelp [24.34131115451651]
  We focus on "reclassification," wherein a platform changes its filtering decision for a review. We compile over 12.5M reviews--more than 2M unique--across over 10k businesses. Our data suggests demographic disparities in reclassifications, with more changes in lower density and low-middle income areas.
  arXiv  Detail & Related papers  (2022-02-18T03:27:53Z)
• An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences [91.3755431537592]
  A new mobile application for image content retrieval and classification for mobile device display is proposed. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.
  arXiv  Detail & Related papers  (2021-08-26T23:44:34Z)
• User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning [64.07820203919283]
  We propose a novel user-guided personalized image aesthetic assessment framework. It leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL) It generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
  arXiv  Detail & Related papers  (2021-06-14T15:19:48Z)
• Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images [79.34600869202373]
  We show that inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations. Our method yields impressive results, outperforming the previous best unsupervised methods by 7.74% and 9.24%.
  arXiv  Detail & Related papers  (2021-05-25T12:22:11Z)

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.