Contextual Combinatorial Cascading Bandits
Authors: Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen
ICML 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real datasets demonstrate the advantage of involving contextual information and position discounts. We evaluate our algorithm, C3-UCB, in a synthetic setting and two real applications. |
| Researcher Affiliation | Collaboration | Shuai Li EMAIL The Chinese University of Hong Kong, Hong Kong Baoxiang Wang EMAIL The Chinese University of Hong Kong, Hong Kong Shengyu Zhang EMAIL The Chinese University of Hong Kong, Hong Kong Wei Chen EMAIL Microsoft Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1 C3-UCB |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is available. |
| Open Datasets | Yes | Movie Lens (Lam & Herlocker, 2015); Rocket Fuel dataset (Spring et al., 2004) |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In experiments, we set the position discounts γk to be γk 1 for some γ. The problem is a contextual cascading bandit with L = 200 items and K = 4, where at each time t the agent recommends K items to the user. At first, we randomly choose a θ Rd 1 with θ 2 = 1 and let θ = ( θ 2). Then at each time t, we randomly assign x t,a Rd 1 with x t,a 2 = 1 to arm a and use xt,a = (x t,a, 1) to be the contextual information for arm a. |