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..
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
Authors: Chang Li, Maarten de Rijke
IJCAI 2019 | Venue PDF | 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 EMAIL |
| 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. |