Project for the Data Intelligence Application course at Politecnico di Milano
The goal is modeling a scenario in which a seller exploits advertising tools to attract more and more users to its website, thus increasing the number of possible buyers. The seller needs to learn simultaneously the conversion rate and the number of users the advertising tools can attract.
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Imagine:
- one product to sell;
- three classes of users, where, for every user, we can observe the values of two binary features (feel free to choose the features and their domains);
- the conversion rate curve of each class of users;
- three subcampaigns, each with a different ad, to advertise the product, and each targeting a different class of users;
- there are three abrupt phases;
- for every abrupt phase and for every subcampaign, the probability distribution over the daily number of clicks for every value of budget allocated to that subcampaign.
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Design a combinatorial bandit algorithm to optimize the budget allocation over the three subcampaigns to maximize the total number of clicks when, for simplicity, there is only one phase. Plot the cumulative regret.
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Design a sliding-window combinatorial bandit algorithm for the case, instead, in which there are the three phases aforementioned. Plot the cumulative regret and compare it with the cumulative regret that a non-sliding-window algorithm would obtain.
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Design a learning algorithm for pricing when the users that will buy the product are those that have clicked on the ads. Assume that the allocation of the budget over the three subcampaigns is fixed and there is only one phase (make this assumption also in the next steps). Plot the cumulative regret.
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Design and run a context generation algorithm for the pricing when the budget allocated to each single subcampaign is fixed. At the end of every week, use the collected data to generate contexts and then use these contexts for the following week. Plot the cumulative regret as time increases. In the next steps, do not use the generated contexts, but use all the data together.
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Design an optimization algorithm combining the allocation of budget and the pricing when the seller a priori knows #] that every subcampaign is associated with a different context and charges a different price for every context. Suggestion: the value per click to use in the knapsack-like problem depends on the pricing, that depends on the number of users of a specific class interested in buying the product. Notice that the two problems, namely, pricing and advertising, can be decomposed since each subcampaign targets a single class of users, thus allowing the computation of the value per click of a campaign only on the basis of the number of clicks generated by that subcampaign. Plot the cumulative regret when the algorithm learns both the conversion rate curves and the performance of the advertising subcampaigns.
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Do the same of Step 6 under the constraint that the seller charges a unique price to all the classes of users. Suggestion: for every possible price, fix this price and repeat the algorithm used in Step 6. Plot the cumulative regret when the algorithm learns both the conversion rate curves and the performance of the advertising subcampaigns.
The chosen product is the subscription to a streaming platform of sport. In the details it offers different championships of basket played in distinct countries.
We differ the users in three classes splitting on two feature: the Country where they live and their Gender.
| Class1 | Class2 | Class3 | |
|---|---|---|---|
| COUNTRY | USA | Europe | Any |
| GENDER | Male | Male | Female |
We distinguish three different phases:
- Phase 1: the launch of the product
- Phase 2: the enter in the market of a new competitor
- Phase 3: the updating of the product with new features to offer
To run the code, the necessary libraries that has to be installed are listed in the "list_packages.txt" file. Please pay attention that the code is tested only for versions of the libraires explicited in the file, for other version the functioning is not guaranteed.
The "run" directory contains all the runnable files of the points of the project(test.py and test_non_stat_bud_env.py files were used only for testing purposes, so please ignore them), especially there is a main file for each request of the project and has a dedicated configuration file. All the configuration files can be found in the "configuration" directory of this project. For each part of the project, the corresponding main file has to be called and the path of the configuration directory has to be specified as first parameter of the main file. In the following lines we provide a generic example, than for each part it is necessary to specify the corresponding main file and the path of the configuration directory:
$ python3 main.py /home/user/DIA-Project/The configuration specifics for this point are contained in the "con_adv.json" file. The main file is "Advertising.py"
The configuration specifics for this point are contained in the "conf_adv_sw.json" file. The main file is "Advertising_SW.py"
The configuration specifics for this point are contained in the "config_pricing.json" file. The main file is "Pricing.py"
The configuration specifics for this point are contained in the "config_pricing.json" file. The main file is "ContextGeneration.py"
The configuration specifics for this point are contained in the "con_adv.json" and "config_pricing.json" file. The main file of the first implementation is "PricingAdvertising.py". The second version main file is "PricAdvFixedPrice.py"
The configuration specifics for this point are contained in the "con_adv.json" and "config_pricing.json" file. The main file of the first implementation is "PricingAdvAggregate.py". The second version main file is "PricingAdvAggregateFixedPrice.py"
In the environment-dev branch, an additional version of the algorithm for this point can be found. In order to run this second version it is necessary to specify only the main file, no configuration file is needed. So:
$ python3 PricingContextGenerationSame2.py