Disposable Linear Bandits for Online Recommendations
Authors: Melda Korkut, Andrew Li4172-4180
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm s performance on a recommendation task based on synthetically generated data. Compared to a number of benchmarks, including Lin UCB and a natural modification of Thompson sampling, our algorithm (solved via the preceding heuristic) achieves as much as 10% lower regret against all competing algorithms. |
| Researcher Affiliation | Academia | Melda Korkut, Andrew Li Tepper School of Business Carnegie Mellon University {melda,aali1}@cmu.edu |
| Pseudocode | Yes | Algorithm 1: Generalized LINUCB (UCBG) and Algorithm 2: Alternating Heuristic |
| Open Source Code | No | The corresponding data can be found in https://github.com/Melda Kor/Disposable Linear Bandits. |
| Open Datasets | Yes | We generated 퐾arms, 푖= 1, . . . , 퐾in 푑 dimensional space where 퐾= 5000 and 푑= 15. Similarly, we generated a set of 휃s that lie in the same space, where the total number of 휃s is 5000. 3The corresponding data can be found in https://github.com/Melda Kor/Disposable Linear Bandits. |
| Dataset Splits | No | The paper mentions synthetically generated data and number of instances but does not specify explicit training, validation, or test dataset splits or cross-validation details. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) are provided for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are listed in the paper. |
| Experiment Setup | Yes | For all experiments, we set the tuning parameter 훼for the heuristic the same as Lin UCB s 푐. In experiments, 훼, 푐= (1/2)푖where 푖= 3, 4, 5. |