Interpolating Item and User Fairness in Multi-Sided Recommendations
Authors: Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Via both theoretical analysis and a numerical case study on real-world data, we demonstrate the efficacy of our framework and method in maintaining platform revenue while ensuring desired levels of fairness for both items and users. |
| Researcher Affiliation | Collaboration | Qinyi Chen1 Jason Cheuk Nam Liang1 Negin Golrezaei1 Djallel Bouneffouf2 1Massachusetts Institute of Technology 2IBM Research {qinyic,jcnliang,golrezae}@mit.edu djallel.bouneffouf@ibm.com |
| Pseudocode | Yes | Algorithm 1 Fair Online Recommendation Algorithm for Multi-Sided Platforms (FORM) |
| Open Source Code | Yes | We have provided code and data for our numerical experiments in the supplementary materials. |
| Open Datasets | Yes | We use an Amazon review dataset [71] from the Clothing, Shoes and Jewelry category. |
| Dataset Splits | No | The paper describes an online learning setting with a total simulation length (T = 2000 rounds) where data is learned sequentially. It does not specify conventional fixed training, validation, or test dataset splits in percentages or counts. |
| Hardware Specification | Yes | All algorithms were implemented in Python 3.7 and run on a Mac Book with a 1.4 GHz Quad-Core Intel Core i5 processor. |
| Software Dependencies | No | The paper mentions 'Python 3.7' and 'CBC solver accessed via the Pu LP library in Python'. While Python has a version, specific version numbers for the CBC solver or Pu LP library are not provided, which is required for a reproducible description of ancillary software. |
| Experiment Setup | Yes | Users are classified into M = 5 types using matrix factorization and k-means clustering on user feature vectors. ... Item revenues ri are uniformly drawn from [0.5, 1.5]. ... Upon arrival, each type-j user is shown an assortment S of up to K = 3 items. |