Multiple-Play Bandits in the Position-Based Model
Authors: Paul Lagrée, Claire Vernade, Olivier Cappe
NeurIPS 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the last section dedicated to experiments, those policies are compared to several benchmarks on both synthetic and realistic data. |
| Researcher Affiliation | Academia | Paul Lagrée LRI, Université Paris Sud Université Paris Saclay EMAIL Claire Vernade LTCI, CNRS, Télécom Paris Tech Université Paris Saclay EMAIL Olivier Cappé LTCI, CNRS Télécom Paris Tech Université Paris Saclay |
| Pseudocode | Yes | Algorithm 1 PBM-PIE |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It references a dataset but not its own code. |
| Open Datasets | Yes | The dataset was provided for KDD Cup 2012 track 2[1] and involves session logs of soso.com, a search engine owned by Tencent. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In order to evaluate our strategies, a simple problem is considered in which K = 5, L = 3, κ = (0.9, 0.6, 0.3) and θ = (0.45, 0.35, 0.25, 0.15, 0.05). ... We conducted a series of 2,000 simulations over this dataset. At the beginning of each run, a query was randomly selected together with corresponding probabilities of scanning positions and arm expectations. |