document: Spatio-temporal models for estimating click-through rate 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 .
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