A benchmarking framework supporting the experiments of real-time bidding optimisation. A description of such problem and benchmarking experiment is here.
In the current version, we implemented the feature engineering for logistic regression CTR estimator and standard bidding functions as described in the above reference. The state-of-the-art bidding function proposed in KDD'14 paper Optimal Real-Time Bidding for Display Advertising is planned to be published here in Dec. 2014.
For any problems, please report the issues here or contact Weinan Zhang.
Please check our GitHub project make-ipinyou-data. After downloading the dataset, by simplying make all you can generate the standardised data which will be used in the bid optimisation tasks.
After successfully generating the data, let's suppose make-ipinyou-data project is placed in the same folder of optimal-rtb project, just like this:
weinan@ZHANG:~/Project$ ls
optimal-rtb make-ipinyou-data
Then under optimal-rtb/scripts/ you can simply run bash demo.sh to train a logistic regression CTR estimator for one campaign data generated in make-ipinyou-data and then perform several bid optimisation algorithms with different parameters.
After running the demo, you could find a file results/rtb.results.1458.best.perf.tsv containing the best performance for each bidding algorithm.
prop clks bids imps budget spend algo para
16 51 227472 97784 2826028 2826032 const 55
16 482 614638 89408 2826028 2804895 lin 130
16 473 614638 38858 2826028 816763 mcpc 1
16 50 199209 77751 2826028 2826041 rand 100
64 16 472727 57030 706507 706508 const 20
64 471 614638 31908 706507 628971 lin 60
64 330 424901 32372 706507 706520 mcpc 1
64 14 614638 52079 706507 689611 rand 30
Here 'prop' means the proportion between the original cost in the test data and the set budget in the test environment. See our benchmarking paper for details.
The code implementation of generating features has a little difference between that in our benchmarking paper: in the orginal dataset, there could be multiple click events for one impression event. In the benchmarking paper, the multiple clicks are directly counted as the number is. In the make-ipinyou-data project, we only count for 1 click even if there are more than one clicks.