Learning Preference Models with Sparse Interactions of Criteria
Authors: Margot Herin, Patrice Perny, Nataliya Sokolovska
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 presents some numerical tests to evaluate the performance of the proposed approach both in terms of computation time and generalizing performances. |
| Researcher Affiliation | Academia | Margot Herin1 , Patrice Perny 1 , Nataliya Sokolovska2 1Sorbonne University, CNRS, LIP6, Paris, France 2 Sorbonne University, CNRS, LCQB, Paris, France |
| Pseudocode | No | The paper describes algorithmic steps and propositions but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. No repository link or explicit code release statement found. |
| Open Datasets | No | In this section we present the results of numerical tests performed on synthetic preference data. Preference data are generated through randomly drawn sparse M obius vectors m (verifying monotonicity constraints) and utilities vectors x, y are uniformly drawn within [0, 1]n. |
| Dataset Splits | No | We set the size of the training sets to |P| + |I| = 500 and of the test sets to |P| = 1000. No explicit mention of a validation set split. |
| Hardware Specification | Yes | All tests are conducted on a 2.8 GHz Intel Core i7 processor with 16GB RAM and we used the mathematical programming Gurobi solver (version 9.1.2). |
| Software Dependencies | Yes | All tests are conducted on a 2.8 GHz Intel Core i7 processor with 16GB RAM and we used the mathematical programming Gurobi solver (version 9.1.2). |
| Experiment Setup | Yes | The regularization parameter λ is set to λ = 1. For the D-IRLS method, the smoothing parameter is set to η = 10 50 and the algorithm terminates when m(k+1) m(k) 2 10 3. Also, coefficients with absolute values smaller than 10 5 are discarded at each iteration. |