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

TL;DR

This paper presents a strategy recommender system for online advertising that improves advertiser performance and platform revenue by learning advertiser preferences and optimizing strategy recommendations using contextual bandit algorithms.

Contribution

The work introduces a prototype system deployed on Taobao that learns advertiser preferences and optimizes strategy recommendations to enhance adoption and effectiveness.

Findings

Increased advertiser performance and platform revenue on Taobao.

Effective learning of advertiser preferences through contextual bandit algorithms.

Improved strategy adoption rates in simulation experiments.

Abstract

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in 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 advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising. We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies. We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.