Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees
Authors: Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
IJCAI 2015 | Venue PDF | 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 EMAIL Philip S. Thomas UMass Amherst and Adobe Research EMAIL Mohammad Ghavamzadeh Adobe Research and INRIA EMAIL |
| 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. |