Optimizing search engine revenue in sponsored search

Authors:
Yunzhang Zhu;Gang Wang;Junli Yang;Dakan Wang;Jun Yan;Jian Hu;Zheng Chen
Affiliations:
Department of Fundamental Science, Tsinghua University, Beijing, China;Microsoft Research Aisa, Beijing, China;Software Engineering Department, Nankai University , Tianjing, China;Computer Science Department, Shanghai Jiaotong University, Shanghai, China;Microsoft Resarch Asia, Beijing, China;Microsoft Resarch Asia, Beijing, China;Microsoft Resarch Asia, Beijing, China
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

Citing 4
Cited 8

Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Eye-tracking analysis of user behavior in WWW search

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to advertise

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Revenue optimization with relevance constraint in sponsored search

Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Learning to advertise: how many ads are enough?

PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Advertiser-centric approach to understand user click behavior in sponsored search

Proceedings of the 20th ACM international conference on Information and knowledge management
An ontology-based approach to Chinese semantic advertising

Information Sciences: an International Journal
Multi-objective optimization for sponsored search

Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
A game- heoretic machine learning approach for revenue maximization in sponsored search

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
From clicking to consideration: A business intelligence approach to estimating consumers' consideration probabilities

Decision Support Systems
Online learning for auction mechanism in bandit setting

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Displaying sponsored ads alongside the search results is a key monetization strategy for search engine companies. Since users are more likely to click ads that are relevant to their query, it is crucial for search engine to deliver the right ads for the query and the order in which they are displayed. There are several works investigating on how to learn a ranking function to maximize the number of ad clicks. In this paper, we address a new revenue optimization problem and aim to answer the question: how to construct a ranking model that can deliver high quality ads to the user as well as maximize search engine revenue? We introduce two novel methods from di fferent machine learning perspectives, and both of them take the revenue component into careful considerations. The algorithms are built upon the click-through log data with real ad clicks and impressions. The extensively experimental results verify the proposed algorithm that can produce more revenue than other methods as well as avoid losing relevance accuracy. To provide deep insight into the importance of each feature to search engine revenue, we extract twelve basic features from four categories. The experimental study provides a feature ranking list according to the revenue benefit of each feature.