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..
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Authors: Junyu Cao, Wei Sun3264-3271
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Numerical Experiments In this section, we ο¬rst investigate the robustness of Algorithm 1 which is our proposed UCB-algorithm for the SC-Bandit problem by comparing how the regret changes with respect to different values of u. Next, we compare our Algorithm 1 and 2 with two benchmarks in the non-contextual and contextual settings respectively. |
| Researcher Affiliation | Collaboration | Junyu Cao University of California, Berkeley Berkeley, California 94720 EMAIL Wei Sun IBM Research Yorktown Height, New York 10591 EMAIL |
| Pseudocode | Yes | Algorithm 1: An exploration-exploitation algorithm for SC-Bandit under marketing fatigue |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a specific repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | No | Experiment setup We consider a setting with N = 30, revenue ri is uniformly distributed between [0,1], abandonment distribution probability p = 0.1 and the cost of abandonment c = 0.5. We present four scenarios, when the valuation u is uniformly generated from [0,0.1], [0,0.2], [0.0.3], and [0.0.5], respectively. |
| Dataset Splits | No | The paper describes generating data for experiments and running simulations over a time horizon T, but it does not specify explicit training, validation, or test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Experiment setup We consider a setting with N = 30, revenue ri is uniformly distributed between [0,1], abandonment distribution probability p = 0.1 and the cost of abandonment c = 0.5. We present four scenarios, when the valuation u is uniformly generated from [0,0.1], [0,0.2], [0.0.3], and [0.0.5], respectively. |