Design a system that predicts the probability a user will click on an ad, used for real-time ad auction bidding. Handle billions of events per day.
Scenario: An ad platform serves 10 billion ad impressions daily across web and mobile. For each ad request, the system must predict the click-through rate (CTR) for hundreds of candidate ads within 10ms to determine auction bids and ad placement.
Your Task: Design the end-to-end CTR prediction pipeline from feature computation to real-time serving.
Your design should address:
Feature engineering from user behavior, ad creative, and contextual signals
Model architecture that handles sparse categorical features at scale
Real-time serving with sub-10ms latency at millions of QPS
Continuous online learning as user behavior shifts
Calibration so predicted probabilities are accurate, not just well-ranked
Think about: Feature interaction modeling, handling delayed conversions, position bias, and the explore-exploit tradeoff for new ads.
The Ad Click-Through Rate (CTR) Prediction problem is a classic example of a binary classification problem in machine learning, where the goal is to predict the probability of a user clicking on an ad. To tackle this problem, it's essential to understand the concepts of feature engineering, model architecture, and real-time serving. Feature engineering involves extracting relevant features from user behavior, ad creative, and contextual signals that can help predict the CTR. Model architecture refers to the design of the machine learning model that can handle sparse categorical features at scale. Real-time serving requires the system to predict the CTR within a very short latency of 10ms to determine auction bids and ad placement.
In the context of CTR prediction, sparse categorical features refer to features that have a large number of unique categories, but most of them appear very infrequently. For example, the category of a user's interests or the type of device they use. Model architectures such as logistic regression, decision trees, and neural networks can be used to handle these features. However, when dealing with billions of events per day, the model needs to be scalable and efficient. This is where distributed computing and parallel processing come into play. The system needs to be able to handle millions of queries per second (QPS) while maintaining a latency of less than 10ms.
To achieve continuous online learning, the system needs to be able to update the model in real-time as user behavior shifts. This can be done using streaming data and incremental learning techniques. Calibration is also crucial to ensure that the predicted probabilities are accurate, not just well-ranked. This can be achieved using techniques such as Platt scaling or Isotonic regression. Additionally, the system needs to handle delayed conversions, position bias, and the explore-exploit tradeoff for new ads. Delayed conversions refer to the fact that the conversion (e.g., a click) may not happen immediately after the ad is shown. Position bias refers to the fact that the position of the ad on the page can affect the CTR. The explore-exploit tradeoff refers to the tradeoff between exploring new ads to learn more about them and exploiting the current knowledge to maximize the CTR.
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Your design approach
Describe your system design approach. Consider components, data flow, and key decisions.
Design questions(Select all that apply)
Q1Why do feature interactions matter for CTR prediction?
Q2How should the system handle billions of sparse categorical features?
Q3What calibration requirement exists for CTR models used in ad auctions?
Q4How should the system handle the feedback loop between predictions and ad display?