Constructive Preference Elicitation Over Hybrid Combinatorial Spaces
Authors: Paolo Dragone, Stefano Teso, Andrea Passerini
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity. |
| Researcher Affiliation | Collaboration | Paolo Dragone University of Trento, Italy TIM-SKIL, Trento, Italy paolo.dragone@unitn.it Stefano Teso KU Leuven, Belgium stefano.teso@cs.kuleuven.be Andrea Passerini University of Trento, Italy andrea.passerini@unitn.it |
| Pseudocode | Yes | Algorithm 1 The Choice Perceptron (CP) algorithm. |
| Open Source Code | Yes | The complete experimental setting can be retrieved from: https://github.com/unitn-sml/choice-perceptron |
| Open Datasets | Yes | We evaluated all methods on the synthetic constructive benchmark introduced in (Teso, Passerini, and Viappiani 2016). ... In the second experiment, we compared CP and SETMARGIN on a much larger recommendation task, also from (Teso, Passerini, and Viappiani 2016). ... Finally, we evaluated CP on a slightly modified version of the touristic trip planning task introduced in (Teso, Dragone, and Passerini 2017). |
| Dataset Splits | Yes | In practice we also employ an adaptive Perceptron step size, which is adapted at each iteration t >= 3 from the set {0.1, 0.2, 0.5, 1, 2, 5, 10} via cross-validation on the collected feedback; it was found to work well empirically. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Gecode via its Mini Zinc interface' but does not specify version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | Yes | As argued in the previous section, for CP we set γ to 1/t in all experiments, in order to allow more exploration earlier on during the search. ... In practice we also employ an adaptive Perceptron step size, which is adapted at each iteration t >= 3 from the set {0.1, 0.2, 0.5, 1, 2, 5, 10} via cross-validation on the collected feedback... We set λ = 1 as in (Teso, Passerini, and Viappiani 2016). ... To help keeping running times low, the query selection procedure of CP is executed with a 20 seconds time cutoff. |