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Statistical Arbitrage Mining for Display Advertising

In real-time bidding (RTB) display advertising, advertisers could choose to pay by performance, e.g., pay per action (CPA), pay per sale (CPS), while publishers normally prefer earning by volume, i.e., selling CPM ad inventories. In such case, the intermediaries, e.g., DSPs or ad networks need to take risk to buy CPM ad inventories from publishers and earn CPA payoff from advertisers. Although this is definitely a non-trivial business, there exist statistical arbitrage opportunities for the intermediaries to maximise their net profit with controlled risk.

This is a repository of the experiment code supporting the paper Statistical Arbitrage Mining for Display Advertising in KDD 2015.

For any problems, please report the issues here or contact Weinan Zhang.

Single Campaign Arbitrage Demo

After pulling the repository, you could start from checking the single campaign arbitrage demo under the folder of scripts by running:

$ bash single_campaign_arbitrage_demo.sh

After running, you could get the performance table printed in the console like:

prop    alpha    algo    profit  cnvs     bids     imps      budget        cost  rratio    para      up
  16  0.00100   const     -2.96     0    80000      740    339072.7      2958.0     0.2     5.0  0.0000
  16  0.00100    rand      0.00     0    80000        0    339072.7         0.0     0.2     5.0  0.0000
  16  0.00100   truth     44.72     3    80000      601    339072.7     11862.0     0.2     1.0  0.0000
  16  0.00100     lin    -93.89    13    59404    10843    339072.7    339097.0     0.2    75.0  0.0000
  16  0.00100    ortb   -131.61    11    55468    12556    339072.7    339091.0     0.2   290.0  0.0000
  16  0.00100    sam1     45.77     6    80000     3148    339072.7     67405.0     0.2    25.0  0.0000
  16  0.00100   sam1c     59.14     6    80000     1303    339072.7     54032.0     0.2    25.0  8.2433
  16  0.00100    sam2     63.92     7    80000     4146    339072.7     68116.0     0.2     7.1  0.0000
  16  0.00100   sam2c     60.63     7    80000     4146    339072.7     71408.9     0.2     7.1  0.7942

Note these results are produced from the very small data (the first 300,000 lines for campaign 1458 in iPinYou). Here alpha is a meaningless column for single campaign task.

Multiple Campaign Arbitrage Demo

For the demo of multiple campaign arbitrage with portfolio selection, please run:

$ bash multiple_campaign_arbitrage_demo.sh

and you can get the performance table printed in the console like:

prop    alpha    algo    profit  cnvs     bids     imps      budget        cost  rratio    para      up
  16  0.10000   const     -4.26     0    80000     1780    410481.3      4264.0     0.2     5.0  0.0000
  16  0.10000    rand    -39.98     1    80000    10304    410481.3    106134.0     0.2    29.0  0.0000
  16  0.10000   truth     44.72     3    80000      601    410481.3     11862.0     0.2     1.0  0.0000
  16  0.10000     lin   -184.14    12    40581    10983    410481.3    410491.0     0.2   105.0  0.0000
  16  0.10000    ortb   -184.16    12    60043    14348    410481.3    410511.0     0.2   290.0  0.0000
  16  0.10000    sam1     45.77     6    80000     3148    410481.3     67405.0     0.2    25.0  0.0000
  16  0.10000   sam1c     49.06     6    80000     2202    410481.3     64108.8     0.2    25.0  3.9500
  16  0.10000    sam2     66.64     7    80000     3905    410481.3     65391.0     0.2     6.5  0.0000
  16  0.10000   sam2c     50.21     6    80000     3100    410481.3     62967.4     0.2     6.5  2.6962
  16  2.00000   const     -3.79     0    80000     1552    410481.3      3787.0     0.2     5.0  0.0000
  16  2.00000    rand     -0.48     0    80000      310    410481.3       477.0     0.2     5.0  0.0000
  16  2.00000   truth     12.26     2    80000     4573    410481.3     72756.0     0.2     1.0  0.0000
  16  2.00000     lin   -184.14    12    40581    10983    410481.3    410491.0     0.2   105.0  0.0000
  16  2.00000    ortb   -184.16    12    60043    14348    410481.3    410511.0     0.2   290.0  0.0000
  16  2.00000    sam1     45.77     6    80000     3148    410481.3     67405.0     0.2    25.0  0.0000
  16  2.00000   sam1c     49.06     6    80000     2202    410481.3     64108.8     0.2    25.0  3.9500
  16  2.00000    sam2     45.56     7    80000     4758    410481.3     86471.0     0.2     6.5  0.0000
  16  2.00000   sam2c     28.05     6    80000     3937    410481.3     85128.1     0.2     6.5  2.6962

Note these results are based on the very small data sample (the first 300,000 lines for campaign portfolio [1458, 2259, 2261] in iPinYou). Here alpha is the risk-averse parameter in campaign portfolio optimisation. We can observe that different alphas result in different arbitrage performance. More detailed information of the campaign portfolio selection can be accessed in data/multiple.campaign.arbitrage.demo.portfolio.txt.

The code in the current version support the experiment in Sections 4.2 (single campaign) and 4.3 (multiple campaign). The dynamic multiple campaign arbitrage task can be based on re-running the multiple campaign code.

Large-scale Experiment

For the large-scale experiment, please first check our GitHub project make-ipinyou-data for pre-processing the 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.