Two-sided fairness in rankings via Lorenz dominance
Authors: Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility. We report experimental results on music and friend recommendation tasks, where we analyze the trade-offs obtained by different methods by looking at different points of their Lorenz curves. Our welfare approach generates a wide variety of trade-offs, and is, in particular, more effective at improving the utility of worse-off users than the baselines. Section 5 Experiments |
| Researcher Affiliation | Collaboration | 1Facebook AI 2LAMSADE, Université PSL, Université Paris Dauphine, CNRS, France virginiedo@fb.com, scd@fb.com, jamal.atif@dauphine.psl.eu, usunier@fb.com |
| Pseudocode | No | The paper describes the Frank-Wolfe algorithm and its steps in text (Section 4) but does not provide a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We present here our experiments with the Lastfm-2k dataset [9, 47], which contains the music listening histories of 1.9k users. We present in App. F.3 results using the Movie Lens-20m dataset [24]. We generate an artificial task based on the Higgs Twitter dataset [15]. |
| Dataset Splits | No | The paper states: 'We split the data in two parts: 80% for training and 20% for testing' (Appendix F.1). However, it does not mention a separate validation split, only training and testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a 'matrix factorization algorithm' but does not specify any software libraries, frameworks, or their version numbers used in the implementation. |
| Experiment Setup | No | The paper mentions aspects of the experimental protocol such as dataset selection and splitting (e.g., 'We select the top 2500 items most listened to, and estimate preferences with a matrix factorization algorithm using a random sample of 80% of the data.'), but it does not specify concrete hyperparameters (e.g., learning rate, batch size, optimizer settings) or other low-level configuration details of the models or algorithms used. |