Simple and Scalable Response Prediction for Display Advertising

By Olivier Chapelle, Eren Manavoglu, Rómer Rosales. 2014

380 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation (2018, background): “This problem is of great practical importance in many application domains where deploying a new policy can be costly or risky, such as medical treatments [26], econometrics [13], recommender systems [19], education [23], Web search [18], advertising and marketing [4, 5, 38, 40].”
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling (2019, background): “In many settings where RL algorithms have already been deployed, e.g., targeted advertising and marketing [Bottou et al., 2013; Tang et al., 2013; Chapelle et al., 2015; Theocharous et al., 2015; Thomas et al., 2017] or medical treatments [Murphy et al., 2001; Ernst et al., 2006; Raghu et al.,…”
Adversarial Attacks on Stochastic Bandits (2018, background): “This is potentially hazardous since stochastic MABs are widely used in the industry to recommend news articles [18], display advertisements [9], improve search results [17], allocate medical treatment [16], and promote users’ well-being [13], among many others.”
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting (2016, methodology): “…[Ahmed et al., 2011, Perlich et al., 2012, Zhang et al., 2016a], knowledge extraction [Ahmed et al., 2011, Yan et al., 2009], relevance feedback [Chapelle, 2014], fraud detection [Stone-Gross et al., 2011, Alrwais et al., 2012, Crussell et al., 2014, Stitelman et al., 2013], incentives and…”
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising (2018, methodology): “Logistic Regression (LR) is probably the most widely used machine learning model on multi-field categorical data for CTR prediction [4, 21].”
Field-aware Factorization Machines for CTR Prediction (2016, background): “Click-through rate (CTR) prediction plays an important role in advertising industry [1, 2, 3].”
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising (2014, methodology): “Due to its easy implementation and state-of-theart performance, LR model has become the most popular one for CTR prediction, especially in industrial systems (Chapelle et al., 2013).”
Scalable hands-free transfer learning for online advertising (2014, methodology): “This ﬁnal“Control B”variant is the methodology used by [4], and is the method most similar to our work in design.”
Toward Predicting the Outcome of an A/B Experiment for Search Relevance (2015, methodology): “In advertising and recommender systems, for instance, it is natural to prefer a system with high adoption/conversion rate and possibly the revenue associated with it ( e.g. , [7, 20]).”
Learning Structured Predictors from Bandit Feedback for Interactive NLP (2016, background): “See Bubeck and CesaBianchi (2012) for an overview. forcement learning2, and has (financially) important real world applications such as online advertising (Chapelle et al., 2014).”
Recent Advances of Large-Scale Linear Classification (2012, methodology): “Quasi Newton method: Currently, a special quasi Newton method called LBFGS [68] has been commonly used in computational advertising [132], [133].”
Automatic ad format selection via contextual bandits (2013, background): “A critical component of the auction is an estimate of the probability that an ad will be clicked on a given impression; also known as click-through rate (CTR) prediction [16, 1, 11, 6, 4, 15].”
Deep & Cross Network for Ad Click Predictions (2017, background): “Linear models [3] are simple, interpretable and easy to scale; however, they are limited in their expressive power.”
CatBoost: unbiased boosting with categorical features (2017, background): “One popular technique for dealing with categorical features in boosted trees is one-hot encoding [7, 25], i.”
A Shared Task on Bandit Learning for Machine Translation (2017, positive): “This learning scenario has been investigated under the names of learning from bandit feedback 1 or reinforcement learning (RL) 2 , and has important real world applications such as online advertising (Chapelle et al., 2014).”