Learning Lexicographic Preference Trees From Positive Examples
Authors: Hélène Fargier, Pierre-François Gimenez, Jérôme Mengin
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {fargier, pgimenez, mengin}@irit.fr |
| 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. |