The Journal of Machine Learning Research
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Advertising keyword suggestion based on concept hierarchy
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Bid generation for advanced match in sponsored search
Proceedings of the fourth ACM international conference on Web search and data mining
Stochastic variability in sponsored search auctions: observations and models
Proceedings of the 12th ACM conference on Electronic commerce
Discrete choice models of bidder behavior in sponsored search
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
| Hi-index | 0.00 |
Sponsored search is a form of online advertising where advertisers bid for placement next to search engine results for specific keywords. As search engines compete for the growing share of online ad spend, it becomes important for them to understand what keywords advertisers value most, and what characteristics of keywords drive value. In this paper we propose an approach to keyword value prediction that draws on advertiser bidding behavior across the terms and campaigns in an account. We provide original insights into the structure of sponsored search accounts that motivate the use of a hierarchical modeling strategy. We propose an economically meaningful loss function which allows us to implicitly fit a linear model for values given observables such as bids and click-through rates. The model draws on demographic and textual features of keywords and takes advantage of the hierarchical structure of sponsored search accounts. Its predictive quality is evaluated on several high-revenue and high-exposure advertising accounts on a major search engine. Besides the general evaluation of advertiser welfare, our approach has potential applications to keyword and bid suggestion.