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..
Learning Lexicographic Preference Trees From Positive Examples
Authors: Hélène Fargier, Pierre-François Gimenez, Jérôme Mengin
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an algorithm to learn several classes of lexicographic preference trees, prove convergence properties of the algorithm, and experiment on both synthetic data and on a real-world bench in the domain of recommendation in interactive configuration. |
| Researcher Affiliation | Academia | H el ene Fargier, Pierre-Franc ois Gimenez, J erˆome Mengin IRIT, CNRS, University of Toulouse, 31000 Toulouse, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Learn a k-LP-tree from a sample H |
| Open Source Code | Yes | Code available at https://github.com/PFGimenez/Ph D. |
| Open Datasets | Yes | Datasets available at http://www.irit.fr/ Helene.Fargier/ BR4CP/benches.html |
| Dataset Splits | Yes | We use a 10 folds cross-validation protocol. |
| Hardware Specification | Yes | The algorithms have been implemented in Java and have been run on a computer with 8 GB of RAM, a single core 3.4 GHz. |
| Software Dependencies | No | The paper states 'The algorithms have been implemented in Java' but does not provide specific version numbers for Java or any other software libraries used. |
| Experiment Setup | Yes | The splitting threshold was set to τ = 20 and the penalty function parameter c set equal to 1. |