Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
Authors: Zhibing Zhao, Ao Liu, Lirong Xia
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on synthetic data demonstrate the effectiveness of MLE on PL with features with tradeoffs between statistical efficiency and computational efficiency. Our experiments on real-world data show the prediction power of PL with features and its mixtures. |
| Researcher Affiliation | Collaboration | 1Microsoft, 555 110TH Ave NE, Bellevue, WA, 98004 2Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a full version on arXiv but does not explicitly state that the source code for the methodology is released or provide a direct link to a code repository. |
| Open Datasets | No | The paper mentions using 'synthetic data' and the 'sushi dataset' but does not provide a specific link, DOI, or formal citation for accessing these datasets. |
| Dataset Splits | No | The paper does not explicitly state the training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | Yes | MLE for PLX -TO was implemented in MATLAB with the built-in fminunc function and tested on a Ubuntu Linux server with Intel Xeon E5 v3 CPUs each clocked at 3.50 GHz. |
| Software Dependencies | No | The paper mentions 'MATLAB with the built-in fminunc function' but does not specify the version number for MATLAB. |
| Experiment Setup | Yes | Fix m = 10 and d = 10. For each agent and each alternative, the feature vector is generated in [ 1, 1] uniformly at random. Each component in β is generated uniformly at random in [ 2, 2]. MLE for PLX -TO was implemented in MATLAB with the built-in fminunc function and tested on a Ubuntu Linux server with Intel Xeon E5 v3 CPUs each clocked at 3.50 GHz. |