Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Authors: Junyu Cao, Wei Sun3264-3271
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Numerical Experiments In this section, we first 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 jycao@berkeley.edu Wei Sun IBM Research Yorktown Height, New York 10591 sunw@us.ibm.com |
| 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. |