Paper Info
Title
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Abstract
ABSTRACTAdvertising 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.
Year
DOI
Venue
2021
10.1145/3447548.3467175
Knowledge Discovery and Data Mining
Keywords
DocType
ISSN
E-commerce, Display Advertisement, Advertising Strategy Recommendation
Conference
KDD 2021, Virtual Event, Singapore
Citations 
PageRank 
References 
0
0.34
0
Authors
13
Name
Order
Citations
PageRank
Liyi Guo100.68
junqi jin21187.95
Haoqi Zhang313.05
Zhenzhe Zheng410319.31
Zhiye Yang500.34
Zhizhuang Xing600.34
Fei Pan7161.52
Wu Fan81731192.15
Lvyin Niu900.34
Haiyang Xu1062.48
Chuan Yu1175.46
Yuning Jiang1252.44
Xiaoqiang Zhu13564.91