2012 MultimediaFeaturesforClickPredi

• (Cheng et al., 2012) ⇒ Haibin Cheng, Roelof van Zwol, Javad Azimi, Eren Manavoglu, Ruofei Zhang, Yang Zhou, and Vidhya Navalpakkam. (2012). “Multimedia Features for Click Prediction of New Ads in Display Advertising.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339652

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Multimedia+Features+for+Click+Prediction+of+New+Ads+in+Display+Advertising
• http://dl.acm.org/citation.cfm?id=2339530.2339652&preflayout=flat#citedby

Quotes

Author Keywords

• Click prediction; complexity measures; display advertising; flash; gmm; image; miscellaneous; multimedia features; new ads; performance measures

Abstract

Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC) and cost-per-action (CPA). An accurate prediction of the probability that users click on ads is a crucial task in NGD advertising because this value is required to compute the expected revenue. State-of-the-art prediction algorithms rely heavily on historical information collected for advertisers, users and publishers. Click prediction of new ads in the system is a challenging task due to the lack of such historical data. The objective of this paper is to mitigate this problem by integrating multimedia features extracted from display ads into the click prediction models. Multimedia features can help us capture the attractiveness of the ads with similar contents or aesthetics. In this paper we evaluate the use of numerous multimedia features (in addition to commonly used user, advertiser and publisher features) for the purposes of improving click prediction in ads with no history. We provide analytical results generated over billions of samples and demonstrate that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultimediaFeaturesforClickPredi	Yang Zhou Ruofei Zhang Haibin Cheng Roelof van Zwol Javad Azimi Eren Manavoglu Vidhya Navalpakkam			Multimedia Features for Click Prediction of New Ads in Display Advertising				10.1145/2339530.2339652		2012