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Introduction

FairDiverse is a unified, comprehensive and efficient benchmark toolkit for fairnes-aware and diversity-aware IR models. It aims to help the researchers to reproduce and develop IR models.

In the lastest release, our library includes 30+ algorithms covering four major categories:

• Pre-processing models

• In-processing models

• Post-processing models

• IR base models

We design a unified pipelines.

For the usage, we use following steps:

The utilized parameters in each config files can be found in following docs:

Parameter Descriptions

Recommendation Parameters

• Parameter settings data pre-processing
  • Required parameters
• Parameter settings for evaluations
  • Evaluation required parameters
• Parameter settings for your new dataset
  • Required parameters
• Parameter settings for in-processing models
  • In-processing required parameters
• Parameter settings for post-processing models
  • Post-processing required parameters

Search Parameters

• Parameter settings for dataset
  • Dataset
  • Column definition
  • Train-Test split
• Parameter settings data pre-processing
  • Required parameters
• Parameter settings for pre-processing models
  • Pre-processing required parameters
  • Ranklib parameters
  • Evaluation parameters
• Parameter settings for post-processing models
  • Post-processing required parameters
  • Supervised and unsupervised model train parameters
  • LLMs-based model parameters
  • Evaluation parameters
  • Log set ups

Custom Your Models (APIs)

For the develop your own recommendation model, you can use following steps:

The Team

FairDiverse is developed and maintained by RUC, UvA.

Here is the list of our lead developers in each development phase.

Time	Version	Lead Developers
Nov. 2024 ~ Feb. 2025	v0.0.1	Chen Xu, Zhirui Deng, Clara Rus

License

FairDiverse uses MIT License.