Linear Submodular Bandits with a Knapsack Constraint
Authors: Baosheng Yu, Meng Fang, Dacheng Tao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct a number of experiments and the experimental results confirm our theoretical analyses. |
| Researcher Affiliation | Academia | Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney Department of Computing and Information Systems, The University of Melbourne |
| Pseudocode | Yes | Algorithm 1 MCSGreedy Algorithm, Algorithm 2 CGreedy Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | following the previous work (Yue and Guestrin 2011), we empirically evaluate our algorithms on simulation dataset by using news article recommendation (Li et al. 2010) as a case study. |
| Dataset Splits | No | The paper uses a "simulation dataset" but does not explicitly provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In this simulation experiment, we randomly generate a d-dimensional vector (x1, x2, . . . , xd) to represent a news article a, where xi (0, 1)(for all i = 1, 2, . . . , d) represents the information coverage of a news article a on the topic i. For each news article a, we assume that it only has a limited number of main topics (xi > 0.5) and noisy topics (xi 0.5). The number of main topics is Nmain = 2 and the number of noise topics is Nnoise = 1. The topics coverage of each article are sampled from a uniform distribution. We randomly generate the w [0, 1]d, which is unknown to our algorithms, to represent a user s interest level in each topic. We also assume that a user will like some topics very much (w (i) 0.8) and will dislike other topics (w (i) 0.1). The costs set C = {c1, c2, . . . , cm} are sampled from the uniform distribution and normal distribution. |