Learning to Rank Based on Analogical Reasoning
Authors: Mohsen Ahmadi Fahandar, Eyke Hüllermeier
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on first experimental results for data sets from various domains (sports, education, tourism, etc.), we conclude that our approach is highly competitive. |
| Researcher Affiliation | Academia | Mohsen Ahmadi Fahandar, Eyke H ullermeier Department of Computer Science Paderborn University Pohlweg 49-51, 33098 Paderborn, Germany ahmadim@mail.upb.de, eyke@upb.de |
| Pseudocode | Yes | Algorithm 1 Analogy-based Pairwise Preferences (APP) and Algorithm 2 Rank Aggregation (RA) are provided. |
| Open Source Code | No | The paper provides a link for datasets ('1available at https://cs.uni-paderborn.de/is/'), but no explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | The data sets1 are collected from various domains (e.g., sports, education, tourism) and comprise different types of feature (e.g., numeric, binary, ordinal). Table 1 provides a summary of the characteristics of the data sets. 1available at https://cs.uni-paderborn.de/is/ |
| Dataset Splits | Yes | We fixed these parameters in an (internal) 2-fold cross validation (repeated 5 times) on the training data, using simple grid search on Sv Sk (i.e., trying all combinations). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'SVM' and 'ERR' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Recall that able2rank has two parameters to be tuned: The type of analogical proportion v Sv, where Sv = {v A, v A , v G, v MM, v AE, v AE }, and the number k Sk of relevant proportions considered for estimating pairwise preferences, where Sk = {10, 15, 20}. We fixed these parameters in an (internal) 2-fold cross validation (repeated 5 times) on the training data, using simple grid search on Sv Sk (i.e., trying all combinations). The combination (v , k ) with the lowest cross-validated error d RL is eventually adopted and used to make predictions on the test data (using the entire training data). The complexity parameter C of SVM is fixed in a similar way using an internal cross-validation. |