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Conversion-Based Dynamic-Creative-Optimization in Native Advertising

arXiv 2211.11524 ↗First posted 13 Nov 2022cs.IR cs.LGcited by 0

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Abstract (arXiv)

Yahoo Gemini native advertising marketplace serves billions of impressions daily, to hundreds millions of unique users, and reaches a yearly revenue of many hundreds of millions USDs. Powering Gemini native models for predicting advertise (ad) event probabilities, such as conversions and clicks, is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. The predicted probabilities are then used in Gemini native auctions to determine which ads to present for every serving event (impression). Dynamic creative optimization (DCO) is a recent Gemini native product that was launched two years ago and is increasingly gaining more attention from advertisers. The DCO product enables advertisers to issue several assets per each native ad attribute, creating multiple combinations for each DCO ad. Since different combinations may appeal to different crowds, it may be beneficial to present certain combinations more frequently than others to maximize revenue while keeping advertisers and users satisfied. The initial DCO offer was to optimize click-through rates (CTR), however as the marketplace shifts more towards conversion based campaigns, advertisers also ask for a {conversion based solution. To accommodate this request, we present a post-auction solution, where DCO ads combinations are favored according to their predicted conversion rate (CVR). The predictions are provided by an auxiliary OFFSET based combination CVR prediction model, and used to generate the combination distributions for DCO ad rendering during serving time. An online evaluation of this explore-exploit solution, via online bucket A/B testing, serving Gemini native DCO traffic, showed a 53.5% CVR lift, when compared to a control bucket serving all combinations uniformly at random.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 34 references that are in the map. First posted on arXiv 13 Nov 2022.

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5 of 34 references.

References · 5

  1. 2017A Tutorial on Thompson Samplingcited by 420
  2. 2013Distributed Exploration in Multi-Armed Banditscited by 46
  3. 2017Field-aware Factorization Machines in a Real-world Online Advertising Systemcited by 21
  4. 2020Modeling Perception Errors towards Robust Decision Making in Autonomous Vehiclescited by 11
  5. 2013OFF-Set: One-pass Factorization of Feature Sets for Online Recommendation in Persistent Cold Start Settingscited by 8

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