Predict ads click-through-rate on a user-ads category level. Check the presentation deck here.
Ad Display/Click Data on Taobao.com
This dataset is provided by Alimama and contains 1.14 million users behavior on Taobao.com platform.
| Table | Description | Feature |
|---|---|---|
| raw_sample | raw training samples | User ID, Ad ID, nonclk, clk, timestamp |
| ad_feature | Ad’s basic information | Ad ID, campaign ID, Cate ID, Brand |
| user_profile | user profile | User ID, age, gender, etc |
| raw_behavior_log | User behavior log | User ID, btag, cate, brand, timestamp |
We used BigQuery to sample 5 million users from the dataset and merge all the tables.
- Logistic Regression, Random Forest with Sklearn
- Logistic Regression, Random Forest with Spark MLlib
- Deep Interest Network Model
Deep Interest Network
It introduces a local activation unit, with which the representation of user interests varies adaptively given different candidate ads.