CTR Prediction

CTR prediction estimates the chance that a user will click an ad or item, helping ranking systems choose what to show and advertisers decide how to bid. Models combine sparse user, item, and context features through feature-interaction networks, sequence models of user interests, and increasingly transformer or multimodal representations for cold-start items. Researchers also address exposure and position bias, score calibration, uncertainty, and evaluation with benchmarks such as Criteo and Avazu as well as online A/B tests. A central challenge is improving accuracy and robustness across long-tail users and changing traffic while keeping inference costs, latency, and calibration suitable for production.