Modeling Attrition in Recommender Systems with Departing Bandits
Authors: Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour6072-6079
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | They conduct an experimental analysis on historical data, while we devise an online learning algorithm with theoretical guarantees. |
| Researcher Affiliation | Collaboration | 1 Blavatnik School of Computer Science, Tel-Aviv University 2 Machine Learning Department, Carnegie Mellon University 3 Google Research |
| Pseudocode | Yes | Algorithm 1: The Departing Bandits Protocol; Algorithm 2: UCB-based algorithm with hybrid radii: UCBHybrid (Jia, Shi, and Shen 2021) |
| Open Source Code | No | The paper mentions a full version 'Ben-Porat et al. 2022' (an arXiv preprint), but does not provide any explicit statement or link to an open-source code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and theoretical guarantees (regret bounds). It does not describe the use of any specific dataset for training or provide access information for one. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental setups or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers, as it does not involve an experimental implementation. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical analysis. It does not describe any experimental setup details such as hyperparameters or training configurations. |