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

Advertisers play an important role in e-commerce platforms, whose advertising expenditures are the main source of revenue for e-commerce platforms. Therefore, providing advertisers with a better advertising experience by reducing their cost of trial and error during ad real-time bidding is crucial to the long-term revenue of e-commerce platforms. To achieve this goal, the advertising platform needs to understand the advertisers' unique marketing demands and actively recommend personalized and optimal advertising strategies for them. In this work, we first deploy a prototype recommender system on Taobao display advertising platform for constant bid and crowd optimization. Then, we propose a novel recommender system for dynamic bidding strategy recommendation, which models the advertiser's strategy recommendation problem as a contextual bandit problem. We use a neural network as the agent to predict the advertisers' demands based on their profile and historical adoption behaviors. Based on the estimated demand, we apply simulated bidding to derive the optimal bidding strategy for recommendation and interact with the advertiser by displaying the possible advertising performance. To solve the exploration/exploitation dilemma, we use Dropout to represent the uncertainty of the network, which approximately equals to conduct Thompson sampling for efficient strategy exploration. Online evaluations show that the system can optimize the advertisers' advertising performance, and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income. Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the adoption rate of advertisers, and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model.