Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees
Authors: Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply these methods to a real PAR problem, both for evaluating the final performance and for optimizing the parameters of the RL algorithm. Our results show that a RL algorithm equipped with these offpolicy evaluation techniques outperforms the myopic approaches. For our experiments we used 2 data sets from the banking industry. |
| Researcher Affiliation | Collaboration | Georgios Theocharous Adobe Research theochar@adobe.com Philip S. Thomas UMass Amherst and Adobe Research phithoma@adobe.com Mohammad Ghavamzadeh Adobe Research and INRIA ghavamza@adobe.com |
| Pseudocode | Yes | Algorithm 1 GREEDYOPTIMIZATION(Xtrain, Xtest, δ, ϵ) : compute a greedy strategy using Xtrain, and predict the 1 δ lower bound on the test data Xtest and the value function. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | For our experiments we used 2 data sets from the banking industry. On the bank website when customers visit, they are shown one of a finite number of offers. The reward is 1 when a user clicks on the offer and 0, otherwise. We extracted/created features, in the categories shown in Table 1. The paper does not provide concrete access information for these datasets. |
| Dataset Splits | Yes | For both algorithms we start with three data sets an Xtrain, Xval and Xtest. We splitted the random strategy data into a test set and a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms like random forest, FQI, Student's t-test, and BCa bootstrap, and even refers to 'MATLAB' but does not specify any version numbers for these software dependencies or libraries. |
| Experiment Setup | Yes | For all experiments we set γ = 0.9 and ϵ = 0.1. |