Position-based Multiple-play Bandit Problem with Unknown Position Bias
Authors: Junpei Komiyama, Junya Honda, Akiko Takeda
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the empirical performance of the proposed algorithms, we conducted computer simulations with synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Junpei Komiyama The University of Tokyo junpei@komiyama.info Junya Honda The University of Tokyo / RIKEN honda@stat.t.u-tokyo.ac.jp Akiko Takeda The Institute of Statistical Mathematics / RIKEN atakeda@ism.ac.jp |
| Pseudocode | Yes | Algorithm 1 PMED and PMED-Hinge Algorithms |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Following the existing work [24, 27], we used the KDD Cup 2012 track 2 dataset [22] that involves session logs of soso.com, a search engine owned by Tencent. |
| Dataset Splits | No | The paper uses synthetic and real-world datasets but does not explicitly provide details about training, validation, and test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'the Gurobi LP solver' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | We set α = 10 for PMED. |