TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity

• URL: http://arxiv.org/abs/2108.08226v1
• Date: Wed, 18 Aug 2021 16:24:40 GMT
• Title: TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity
• Authors: Shaunak Mishra, Changwei Hu, Manisha Verma, Kevin Yen, Yifan Hu and Maxim Sviridenko
• Abstract summary: We propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak. As suggestions for ad text improvement, TSI shows anonymized versions of superior ads (higher predicted CTR) in the neighborhood.
• Score: 16.10904771281746
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Coming up with effective ad text is a time consuming process, and particularly challenging for small businesses with limited advertising experience. When an inexperienced advertiser onboards with a poorly written ad text, the ad platform has the opportunity to detect low performing ad text, and provide improvement suggestions. To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak. In addition, as suggestions for ad text improvement, TSI shows anonymized versions of superior ads (higher predicted CTR) in the neighborhood. For (i), we propose a BERT based text-to-CTR model trained on impressions and clicks associated with an ad text. For (ii), we propose a sentence-BERT based semantic-ad-similarity model trained using weak labels from ad campaign setup data. Offline experiments demonstrate that our BERT based text-to-CTR model achieves a significant lift in CTR prediction AUC for cold start (new) advertisers compared to bag-of-words based baselines. In addition, our semantic-textual-similarity model for similar ads retrieval achieves a precision@1 of 0.93 (for retrieving ads from the same product category); this is significantly higher compared to unsupervised TF-IDF, word2vec, and sentence-BERT baselines. Finally, we share promising online results from advertisers in the Yahoo (Verizon Media) ad platform where a variant of TSI was implemented with sub-second end-to-end latency.

Related papers

• Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text [61.22649031769564]
  We propose a novel framework, paraphrased text span detection (PTD) PTD aims to identify paraphrased text spans within a text. We construct a dedicated dataset, PASTED, for paraphrased text span detection.
  arXiv  Detail & Related papers  (2024-05-21T11:22:27Z)
• AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness [25.531489722164178]
  We propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGAN-based facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations to click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads.
  arXiv  Detail & Related papers  (2023-09-15T04:52:49Z)
• Long-Term Ad Memorability: Understanding & Generating Memorable Ads [54.23854539909078]
  There has been no large-scale study on the memorability of ads. We release the first memorability dataset, LAMBDA, consisting of 1749 participants and 2205 ads covering 276 brands.
  arXiv  Detail & Related papers  (2023-09-01T10:27:04Z)
• Boost CTR Prediction for New Advertisements via Modeling Visual Content [55.11267821243347]
  We exploit the visual content in ads to boost the performance of CTR prediction models. We learn the embedding for each visual ID based on the historical user-ad interactions accumulated in the past. After incorporating the visual ID embedding in the CTR prediction model of Baidu online advertising, the average CTR of ads improves by 1.46%, and the total charge increases by 1.10%.
  arXiv  Detail & Related papers  (2022-09-23T17:08:54Z)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning [14.912117221662054]
  We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. Experiments on industrial datasets show that CREATER significantly outperforms current approaches.
  arXiv  Detail & Related papers  (2022-05-18T14:17:04Z)
• VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search [6.107273836558503]
  We propose VisualTextRank as an unsupervised method to augment input ad text using semantically similar ads. VisualTextRank builds on prior work on graph based context extraction. Online tests with a simplified version led to a 28.7% increase in the usage of stock image search.
  arXiv  Detail & Related papers  (2021-08-05T16:47:21Z)
• Auditing for Discrimination in Algorithms Delivering Job Ads [70.02478301291264]
  We develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn.
  arXiv  Detail & Related papers  (2021-04-09T17:38:36Z)
• Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure [24.13017090236483]
  Ad creatives with enjoyable visual appearance may increase the click-through rate (CTR) of products. We propose an Adaptive and Efficient ad creative Selection framework based on a tree structure. Based on the tree structure, Thompson sampling is adapted with dynamic programming, leading to efficient exploration for potential ad creatives with the largest CTR.
  arXiv  Detail & Related papers  (2021-03-02T03:39:41Z)
• An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis [67.58267006314415]
  We present a large-scale analysis on ad-related user feedback from App Store and Google Play. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage. Some ad issue types are addressed more quickly by developers than other ad issues.
  arXiv  Detail & Related papers  (2020-08-22T05:38:24Z)
• Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement [26.70647666598025]
  We study approaches to refine the given ad text and image by: (i) generating new ad text, (ii) recommending keyphrases for new ad text, and (iii) recommending image tags (objects in image) Based on A/B tests conducted by multiple advertisers, we form pairwise examples of inferior and superior ad creatives. We also share broadly applicable insights from our experiments using data from the Yahoo Gemini ad platform.
  arXiv  Detail & Related papers  (2020-08-17T16:46:28Z)

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.