A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
Authors: Omer Ben-Porat, Moshe Tennenholtz
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties. |
| Researcher Affiliation | Academia | Omer Ben-Porat and Moshe Tennenholtz Technion Israel Institute of Technology Haifa 32000 Israel {omerbp@campus,moshe@ie}.technion.ac.il |
| Pseudocode | Yes | Algorithm 1: Shapley Mediator |
| Open Source Code | No | The paper provides a link to its arXiv preprint (https://arxiv.org/abs/1806.00955) but no explicit statement or link for open-source code for the methodology. |
| Open Datasets | No | The paper is theoretical and presents examples with illustrative data rather than using or providing access to publicly available datasets for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |