Elements of information theory
Elements of information theory
Convex Optimization
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Pay-per-action model for online advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
A noisy-channel approach to contextual advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Estimating Ad Clickthrough Rate through Query Intent Analysis
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
What happens after an ad click?: quantifying the impact of landing pages in web advertising
Proceedings of the 18th ACM conference on Information and knowledge management
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Ranking for the conversion funnel
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
Proceedings of the 2013 international conference on Intelligent user interfaces
Traffic quality based pricing in paid search using two-stage regression
Proceedings of the 22nd international conference on World Wide Web companion
Predictive model performance: offline and online evaluations
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
CTR prediction for contextual advertising: learning-to-rank approach
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
| Hi-index | 0.00 |
In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the conversion rate (CVR). CVRs measure the proportion of certain users who take a predefined, desirable action, such as a purchase, registration, download, etc.; as compared to simply page browsing. We provide a detailed analysis of conversion rates in the context of non-guaranteed delivery targeted advertising. In particular we focus on the post-click conversion (PCC) problem or the analysis of conversions after a user click on a referring ad. The key elements we study are the probability of a conversion given a click in a user/page context, P(conversion | click, context). We provide various fundamental properties of this process based on contextual information, formalize the problem of predicting PCC, and propose an approach for measuring attribute relevance when the underlying attribute distribution is non-stationary. We provide experimental analyses based on logged events from a large-scale advertising platform.