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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity
Authors: John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu1877-1884
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also run experiments on real-world and synthetic datasets to validate our algorithms. |
| Researcher Affiliation | Academia | John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu EMAIL University of Maryland, College Park, MD, USA |
| Pseudocode | Yes | Algorithm 1 A CR-based algorithm (CR-ALG)... Algorithm 2 An MMP-based online algorithm (MMP-ALG) |
| Open Source Code | Yes | Full code can be found at https://bitbucket.org/karthikabinav/ submodularmatching/src/master/ |
| Open Datasets | Yes | We use the Movie Lens dataset (Harper and Konstan 2016) for our purposes10. |
| Dataset Splits | No | The paper mentions preprocessing steps like using collaborative filtering to complete a matrix of ratings, but it does not explicitly provide details about training, validation, or test dataset splits for its own experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper provides a link to the code, but it does not explicitly list specific software components with version numbers in the text to describe ancillary software dependencies for reproducibility. |
| Experiment Setup | Yes | First, we compare our algorithms against the baselines by varying two parameters B and η. B represents the number of times we can match a movie to an user and η represents the number of movies matched to any user on arrival (in the theory B = 1, η = 1, but we experiment with different values)... For every user we choose a random arrival probability (ensuring that the sum of arrival probabilities equals 1). |