Non-monotone Sequential Submodular Maximization
Authors: Shaojie Tang, Jing Yuan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical evaluations further validate the effectiveness of our proposed algorithms in the domain of video recommendations. |
| Researcher Affiliation | Academia | 1Naveen Jindal School of Management, University of Texas at Dallas 2 Department of Computer Science and Engineering, University of North Texas |
| Pseudocode | Yes | Algorithm 1: Sampling-Greedy |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology was found. |
| Open Datasets | Yes | We evaluate our algorithms and benchmarks on the latest Movie Lens dataset (Harper and Konstan 2015), consisting of 62, 423 movies, of which 13, 816 have both user-generated tags and ratings. |
| Dataset Splits | No | The paper describes the dataset used but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries with their versions). |
| Experiment Setup | Yes | We set η = 35 and adjust the parameters α and β to ensure that the two components in fj( ) are roughly equal in magnitude. In each experimental set, we perform 100 rounds and present the average results as follows. |