Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
Authors: Chang Li, Maarten de Rijke
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate their performance on a realworld web search click dataset. |
| Researcher Affiliation | Academia | Chang Li and Maarten de Rijke University of Amsterdam {c.li, derijke}@uva.nl |
| Pseudocode | Yes | Algorithm 1: UCB-type algorithm for Cascading nonstationary bandits. |
| Open Source Code | No | The paper mentions using a tool named Py Click and provides a link to its GitHub repository (https://github.com/markovi/Py Click), but it does not state that the authors' own code for the described methodology or experiments is open-sourced or provided. |
| Open Datasets | Yes | We evaluate Cascade DUCB and Cascade SWUCB on the Yandex click dataset,2 which is the largest public click collection. 2https://academy.yandex.ru/events/data analysis/relpred2011 |
| Dataset Splits | No | The paper discusses simulation setup and online learning steps but does not explicitly describe train/validation/test dataset splits with percentages or sample counts for reproducing the experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using "Py Click" (Py Click.3) but does not provide a specific version number for it or any other software dependencies crucial for reproduction. |
| Experiment Setup | Yes | In experiments, we set = 0.5, γ = 1 1/(4pn) and = 2 p n ln(n), the values that roughly minimize the upper bounds. In our experiment, we set m1 = m2 = 10k and choose 10 breakpoints. In total, we run experiments for 100k steps. |