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

by   Shaunak Mishra, et al.

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.


page 1

page 2

page 3

page 4


Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement

In the online advertising industry, the process of designing an ad creat...

VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search

Numerous online stock image libraries offer high quality yet copyright f...

SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance

Ad relevance modeling plays a critical role in online advertising system...

Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

There is a perennial need in the online advertising industry to refresh ...

AutoADR: Automatic Model Design for Ad Relevance

Large-scale pre-trained models have attracted extensive attention in the...

Cost-sensitive Learning of Deep Semantic Models for Sponsored Ad Retrieval

This paper formulates the problem of learning a neural semantic model fo...

Managing App Install Ad Campaigns in RTB: A Q-Learning Approach

Real time bidding (RTB) enables demand side platforms (bidders) to scale...

Please sign up or login with your details

Forgot password? Click here to reset