We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers through reducing their costs of trial and error for discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertisers' marketing objectives, and then recommend the corresponding strategies to fulfill this objective. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, recommending bid prices and targeted users to advertisers. We further augment this prototype system by directly revealing the advertising performance, and then infer the advertisers' marketing objectives through their adoptions of different recommending advertising performance. We use the techniques from context bandit to jointly learn the advertisers' marketing objectives and the recommending strategies. Online evaluations show that the designed advertising strategy recommender system can optimize the advertisers' advertising performance and increase the platform's revenue. Simulation experiments based on Taobao online bidding data show that the designed contextual bandit algorithm can effectively optimize the strategy adoption rate of advertisers.