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📊 Criteo 1TB Click Logs Dataset
This dataset contains feature values and click feedback for millions of display ads. Its primary purpose is to benchmark algorithms for clickthrough rate (CTR) prediction.
It is similar, but larger than the dataset released for the Display Advertising Challenge hosted by Kaggle:
🔗 Kaggle Criteo Display Advertising Challenge
📁 Full Description
This dataset contains 24 files, each corresponding to one day of data.
🏗️ Dataset Construction
- The training data spans 24 days of Criteo traffic.
- Each row represents a display ad served by Criteo.
- The first column indicates whether the ad was clicked (1) or not clicked (0).
- Both positive (clicked) and negative (non-clicked) examples have been subsampled, though at different rates to keep business confidentiality.
🧱 Features
13 integer features
Mostly count-based; represent numerical properties of the ad, user, or context.26 categorical features
Values are hashed into 32-bit integers for anonymization.
The semantic meaning of these features is undisclosed.
⚠️ Some features may contain missing values.
🧾 Data Format
- Rows are chronologically ordered
- Columns are tab-separated and follow this schema:
<label> <int_feature_1> ... <int_feature_13> <cat_feature_1> ... <cat_feature_26> - If a value is missing, the field is simply left empty.
🔄 Differences from Kaggle Challenge Dataset
- 📅 The data covers a different time period
- 🔄 Subsampling ratios differ
- 🔢 Ordering of features is different
- 🧮 Some features have different computation methods
- 🔐 Hash function for categorical features has changed
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