Simple and Scalable Response Prediction for Display Advertising
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Summary
A machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising and provides models with state-of-the-art accuracy.
- Type
- article
- Published
- 2014-12-29
- Cited by
- 384
- References
- 74
- Access
- Open access
- OpenAlex
- https://openalex.org/W1985759455
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3182689
Keywords
Computer science, Terabyte, Scalability, Display advertising, Conversation
References
- Predicting Click-Through Rate Using Keyword Clusters
- Using Maximum Entropy for Text Classification
- Stochastic and Contingent Payment Auctions
- Pattern Recognition and Machine Learning
- GraphLab: A New Framework For Parallel Machine Learning
- Estimating rates of rare events with multiple hierarchies through scalable log-linear models
- Post-click conversion modeling and analysis for non-guaranteed delivery display advertising
- Data Analysis Using Regression and Multilevel/Hierarchical Models
- Temporal multi-hierarchy smoothing for estimating rates of rare events
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
- Multimedia features for click prediction of new ads in display advertising
- Efficient noise-tolerant learning from statistical queries
- Finding the right consumer: optimizing for conversion in display advertising campaigns
- One-Armed Bandit Problems with Covariates
- A scalable modular convex solver for regularized risk minimization
- Stochastic gradient boosted distributed decision trees
- Asymptotically efficient adaptive allocation rules
- Online learning from click data for sponsored search
- Response prediction using collaborative filtering with hierarchies and side-information
- ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES
Cited by
- Online Advertising: Forecasting and Synthesising Web Activity Based On Historical Data
- Learning to target advertisements at Spotify
- Offline Evaluation of Response Prediction in Online Advertising Auctions
- Machine Learning at Scale
- Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data
- Using Neural Networks for Click Prediction of Sponsored Search
- Viewability Prediction for Online Display Ads
- PREDICTING LEARNERS PERFORMANCE USING ARTIFICIAL NEURAL NETWORKS IN LINEAR PROGRAMMING INTELLIGENT TUTORING SYSTEM
- Modeling delayed feedback in display advertising
- Recent Advances of Large-Scale Linear Classification
- Smart Pacing for Effective Online Ad Campaign Optimization
- Scalable hands-free transfer learning for online advertising
- Automatic ad format selection via contextual bandits
- Toward Predicting the Outcome of an A/B Experiment for Search Relevance
- Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising
- Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising
- Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization
- Bandit structured prediction for learning from partial feedback in statistical machine translation
- Adslot Mining for Online Display Ads
- Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
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