🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.
They popped up when you were browsing, they popped up when you were streaming videos, they popped up when you were playing an intense video game. Did you give in? Did you find those advertisements catchy enough to click! Well we made a challenge out of it! Given information about an advertisement, can you predict whether it will be clicked upon?
Understand with code! Here is
getting started code for you.
The dataset provided contains advertisements that were shown along side search results and whether or not they were clicked on. For each advertisement 11 properties are given:
Click- 1 if the add was clicked, 0 if it was not clicked
impression- number of sessions in which the Ad was shown to the userID who issued the query
url_hash- a hash of the url on which the ad was displayed
AdID- an id uniquely identifying an ad
advertiserID- an id uniquely identifying an advertiser
Depth- number of ads displayed to a user in a session
Position- order of an ad in a display
query_id- an id uniquely identifying a query
keyword_id- an id uniquely identifying a search keyword
title_id- an id uniquely identifying the title of the ad
description_id- an id uniquely identifying a description of the ad
user_id- an id uniquely identifying a user
Following files are available in the
40000samples) This csv file contains the attributes describing an advertisement along with the binary value denoting whether or not the advertisement was clicked on .
10000samples) File that will be used for actual evaluation for the leaderboard score but does not have the binary value denoting whether or not the advertisement was clicked on.
- Prepare a CSV containing header as
clickand predicted value as digit
1respectively denoting whether or not the corresponding ad will be clicked upon.
- Name of the above file should be
- Sample submission format available at
sample_submission.csvin the resorces section.
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
During evaluation F1 score will be used to test the efficiency of the model where,
- 💪 Challenge Page: https://www.aicrowd.com/challenges/adclk
- 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/adclk/discussion
- 🏆 Leaderboard: https://www.aicrowd.com/challenges/adclk/leaderboards