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 [1].
Disposable Linear Bandits for Online Recommendations
Authors: Melda Korkut, Andrew Li4172-4180
AAAI 2021 | Venue PDF | 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 modi๏ฌcation 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 EMAIL |
| 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 ํพarms, ํ= 1, . . . , ํพin ํ dimensional space where ํพ= 5000 and ํ= 15. Similarly, we generated a set of ํs that lie in the same space, where the total number of ํs 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 ํผfor the heuristic the same as Lin UCB s ํ. In experiments, ํผ, ํ= (1/2)ํwhere ํ= 3, 4, 5. |