Online Certification of Preference-Based Fairness for Personalized Recommender Systems
Authors: Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier6532-6540
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also study the tradeoffs achieved on real-world recommendation datasets. ... In Sec. 5, we investigate the trade-offs achieved on real-world datasets. ... We present experiments describing sources of envy (Sec. 5.1) and evaluating the auditing algorithm OCEF on two recommendation tasks (Sec. 5.2). |
| Researcher Affiliation | Collaboration | Virginie Do1,2, Sam Corbett-Davies2, Jamal Atif1, Nicolas Usunier2 1LAMSADE, Universit e PSL, Universit e Paris Dauphine, CNRS, France 2Meta AI |
| Pseudocode | Yes | Algorithm 1: OCEF algorithm. ... Algorithm 2: AUDIT algorithm. |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the methodology described, nor does it state that the code is being released. |
| Open Datasets | Yes | We create a music recommendation task based on the Last.fm dataset from Cantador et al. (2011)... We also address movie recommendation with the Movie Lens-1M dataset (Harper and Konstan 2015) |
| Dataset Splits | Yes | the simulated recommender system estimates relevance scores using low-rank matrix completion (Bell and Sejnowski 1995) on a training sample of 20% of the ground truth preferences |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running its experiments. |
| Software Dependencies | No | Using the Python library Implicit: https://github.com/benfred/ implicit (MIT License). This mentions a library but does not specify its version number, nor does it list other software dependencies with versions. |
| Experiment Setup | Yes | Recommendations are given by a fixed-temperature softmax policy over the predicted scores. ... We vary the number of latent factors of the matrix completion model and evaluate a softmax policy with inverse temperature set to 5. ... matrix completion with 48 latent factors. ... inverse temperature equal to 5 or 10. |