论文标题
基于转换的本地广告中的动态创造性优化
Conversion-Based Dynamic-Creative-Optimization in Native Advertising
论文作者
论文摘要
Yahoo Gemini本地广告市场每天为数亿个唯一用户提供数十亿美元的印象,并获得数亿美元的年收入。为预测广告(AD)事件概率(例如转换和点击)的Demini本机模型提供动力 - 基于功能增强的基于功能的协作过滤(CF)事件预测算法。然后,在双子座天然拍卖中使用预测的概率来确定每个服务事件都会呈现哪些AD(印象)。动态创意优化(DCO)是两年前推出的Gemini本地产品,并越来越受到广告商的关注。 DCO产品使广告商可以每个本机广告属性发行几个资产,从而为每个DCO AD创建多个组合。由于不同的组合可能吸引不同的人群,因此比其他人更频繁地提供某些组合可能会有益,以最大程度地提高收入,同时使广告商和用户满意。最初的DCO优惠是优化点击率(CTR),但是随着市场向基于转换的广告系列的转变,广告商还要求提供基于转换的解决方案。为了适应此请求,我们提出了一种拍卖后解决方案,其中DCO ADS组合根据其预测的转换率(CVR)受到青睐。这些预测由基于辅助偏移的组合CVR预测模型提供,并用于生成服务时间期间DCO AD渲染的组合分布。通过在线存储桶A/B测试(服务双子座本地DCO流量)的在线评估,与对照桶的随机服务所有组合相比,CVR升降率为53.5%。
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.