A Representation Theory for Ranking Functions
Authors: Harsh H Pareek, Pradeep K Ravikumar
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our experiments, we consider the information retrieval learning to rank task, where we are given a training set consisting of n queries. ... Table 1 shows some results across LETOR datasets which show improvements over the base rankers. |
| Researcher Affiliation | Academia | Harsh Pareek, Pradeep Ravikumar Department of Computer Science University of Texas at Austin {harshp,pradeepr}@cs.utexas.edu |
| Pseudocode | No | The paper describes the functional form of the ranking function (Equation 14) but does not present it as structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper mentions using 'Rank Lib' and provides a link to its source, but there is no explicit statement or link indicating that the authors' own methodology code is open-source or publicly available. |
| Open Datasets | Yes | We use the LETOR 3.0 collection [23], which contains the OHSUMED dataset and the Gov collection: HP2003/04, TD2003/04, NP2003/04, which respectively correspond to the listwise Homepage Finding, Topic Distillation and Named Page Finding tasks. |
| Dataset Splits | Yes | LETOR contains 5 predefined folds with training, validation and test sets. We use these directly and report averaged results on the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | To obtain the scores b for the baseline pointwise ranking function, we used Ranklib v2.1-patched with its default parameter values. |
| Experiment Setup | Yes | For the ℓ2 regularization parameter, we pick a C from [0, 1e-5,1e-2, 1e-1, 1, 10, 1e2,1e3] tuning for maximum NDCG@10 on the validation set. We used gradient descent on w to fit parameters. ... and used the initial value w = 0 for our experiments. |