  In this work, we introduce the notion of Context-Based Prediction Models. A
Context-Based Prediction Model determines the probability of a user's action
(such as a click or a conversion) solely by relying on user and contextual
features, without considering any specific features of the item itself. We have
identified numerous valuable applications for this modeling approach, including
training an auxiliary context-based model to estimate click probability and
incorporating its prediction as a feature in CTR prediction models. Our
experiments indicate that this enhancement brings significant improvements in
offline and online business metrics while having minimal impact on the cost of
serving. Overall, our work offers a simple and scalable, yet powerful approach
for enhancing the performance of large-scale commercial recommender systems,
with broad implications for the field of personalized recommendations.
