Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Finite-Time Regret Bounds for the Multiarmed Bandit Problem
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy of search logs: how hard is search? with personalization? with backoff?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Popularity, novelty and attention
Proceedings of the 9th ACM conference on Electronic commerce
Software for Data Analysis: Programming with R
Software for Data Analysis: Programming with R
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Proceedings of the fourth ACM international conference on Web search and data mining
Click shaping to optimize multiple objectives
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to advertise: how many ads are enough?
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
User action interpretation for personalized content optimization in recommender systems
Proceedings of the 20th ACM international conference on Information and knowledge management
Relational click prediction for sponsored search
Proceedings of the fifth ACM international conference on Web search and data mining
Online modeling of proactive moderation system for auction fraud detection
Proceedings of the 21st international conference on World Wide Web
Fast mining and forecasting of complex time-stamped events
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding trending local topics in search queries for personalization of a recommendation system
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Repetition-aware content placement in navigational networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Ad click prediction: a view from the trenches
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic ad format selection via contextual bandits
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Predicting response in mobile advertising with hierarchical importance-aware factorization machine
Proceedings of the 7th ACM international conference on Web search and data mining
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We propose novel spatio-temporal models to estimate click-through rates in the context of content recommendation. We track article CTR at a fixed location over time through a dynamic Gamma-Poisson model and combine information from correlated locations through dynamic linear regressions, significantly improving on per-location model. Our models adjust for user fatigue through an exponential tilt to the first-view CTR (probability of click on first article exposure) that is based only on user-specific repeat-exposure features. We illustrate our approach on data obtained from a module (Today Module) published regularly on Yahoo! Front Page and demonstrate significant improvement over commonly used baseline methods. Large scale simulation experiments to study the performance of our models under different scenarios provide encouraging results. Throughout, all modeling assumptions are validated via rigorous exploratory data analysis.