Display Advertising Challenge. Predict click-through rates on display ads.
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------ Display Advertising Challenge ------ Dataset: dac-v1 This dataset contains feature values and click feedback for millions of display ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction. It has been used for the Display Advertising Challenge hosted by Kaggle: https://www.kaggle.com/c/criteo-display-ad-challenge/ =================================================== Full description: This dataset contains 2 files: train.txt test.txt corresponding to the training and test parts of the data. ==================================================== Dataset construction: The training dataset consists of a portion of Criteo's traffic over a period of 7 days. Each row corresponds to a display ad served by Criteo and the first column is indicates whether this ad has been clicked or not. The positive (clicked) and negatives (non-clicked) examples have both been subsampled (but at different rates) in order to reduce the dataset size. There are 13 features taking integer values (mostly count features) and 26 categorical features. The values of the categorical features have been hashed onto 32 bits for anonymization purposes. The semantic of these features is undisclosed. Some features may have missing values. The rows are chronologically ordered. The test set is computed in the same way as the training set but it corresponds to events on the day following the training period. The first column (label) has been removed. ==================================================== Format: The columns are tab separeted with the following schema: <label> <integer feature 1> ... <integer feature 13> <categorical feature 1> ... <categorical feature 26> When a value is missing, the field is just empty. There is no label field in the test set. ==================================================== Dataset assembled by Olivier Chapelle (firstname.lastname@example.org)