Representations of quasi-Newton matrices and their use in limited memory methods
Mathematical Programming: Series A and B
A maximum entropy approach to natural language processing
Computational Linguistics
Computer Vision
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Color spaces for computer graphics
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM SIGGRAPH 2006 Papers
Natural color image enhancement and evaluation algorithm based on human visual system
Computer Vision and Image Understanding
Syntactic Information Retrieval
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Continuous visual vocabulary modelsfor pLSA-based scene recognition
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Feature hashing for large scale multitask learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning the click-through rate for rare/new ads from similar ads
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
Prediction of favourite photos using social, visual, and textual signals
Proceedings of the international conference on Multimedia
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
The impact of visual appearance on user response in online display advertising
Proceedings of the 21st international conference companion on World Wide Web
CTR prediction for contextual advertising: learning-to-rank approach
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
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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.