Content Provider Dynamics and Coordination in Recommendation Ecosystems
Authors: Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we investigate the dynamics of content creation using a game-theoretic lens. Employing a stylized model that was recently suggested by other works, we show that the dynamics will always converge to a pure Nash Equilibrium (PNE), but the convergence rate can be exponential. We complement the analysis by proposing an efficient PNE computation algorithm via a combinatorial optimization problem that is of independent interest. |
| Researcher Affiliation | Academia | Omer Ben-Porat Technion Haifa 32000 Israel omerbp@campus.technion.ac.il Itay Rosenberg Technion Haifa 32000 Israel itayrose@campus.technion.ac.il Moshe Tennenholtz Technion Haifa 32000 Israel moshet@ie.technion.ac.il |
| Pseudocode | Yes | Algorithm 1: PNE computation |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets. It uses abstract model parameters like 'demand distribution D' and 'quality matrix Q', not empirical datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, so there are no training, validation, or test splits mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and algorithm design; therefore, it does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or versions used for implementation or analysis. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Consequently, there are no details about experimental setup, hyperparameters, or training configurations. |