Impedance coupling in content-targeted advertising
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
Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
VOXSUP: a social engagement framework
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Psychological advertising: exploring user psychology for click prediction in sponsored search
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Sponsored search is the major business model of commercial search engines. The number of clicks on ads is a key indicator of success for both advertisers and search engines, and increasing ad clicks is a goal of both of them. Many existing works stand on the view of search engines concerning how to help search engines to earn more revenue by accurately predicting ad clicks. Unlike the existing works, this paper aims at understanding user clicks on ads from "the view of advertisers", in order to help advertisers to improve their ad quality and therefore advertising effectiveness. To do this, a factor graph model is proposed, which considers two advertiser-controllable factors to understand user click behaviors: the relevance between a query and an ad, which has been well studied in previous literatures, and the "attractiveness" of the ad, which is a newly-proposed concept. The proposed model can be used to predict user clicks and also to mine a set of attractive words that could be leveraged to improve the quality of the ads. We have verified the effectiveness of the proposed approach using real-world datasets, through quantitative evaluations and informative case studies.