Optimized Cost per Click in Taobao Display Advertising

By Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, Kun Gai. 2017

171 citations recorded by the source corpus.

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Xiaoqiang Zhu
2. Kun Gai
3. Guorui Zhou

In Marketing

1. Kun Gai
2. Han Zhu
3. Han Li
4. Junqi Jin

Citations in the corpus

• Deep Interest Network for Click-Through Rate Prediction (2017, background): “An variation of user weighted AUC is introduced in [7, 13] which measures the goodness of intra-user order by averaging AUC over users and is shown to be more relevant to online performance in display advertising system.”
• Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate (2018, methodology): “For example, predicted CVR is used in OCPC (optimized cost-per-click) advertising to adjust bid price per click to achieve a win-win of both platform and advertisers [4].”
• Learning Tree-based Deep Model for Recommender Systems (2018, methodology): “And after ranking by strategy [17, 35], several items are ultimately impressed to user.”