Towards Fair Allocation In Social Commerce

This repository contains official implementation of the algorithms defined in our paper titled, Anjali Gupta, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Nadkarni, Narendra Varma and Muthusamy Chelliah, "Towards Fair Allocation in Social Commerce Platforms", in The Web Conference 2023 (formerly known as WWW).

Use bellow commands to run algorithms

cd code_www
python [algoname].py [Instance] [L] [alpha] [Epsilon] [R2_option]

Where

• Instance is the dataset instance.
• For alpha, take a value greator than 0 but less than or equal to 1. (0 <alpha<=1)
• R2_option = 1 for R2 = number-of-re-sellers, R2_option = 2 for R2 = 2 * R1, and R2_option = 3 for R2 = R1 + 1

for example: To run SEAL use:

python SEAL.py 1 15 1 0 1

To run GreedyNash use:

python GreedyNash.py 1 15 1 0 1

To run NashMax use:

python NashMax.py 1 15 1 3 2

To run RevMax use:

python RevMax.py 1 15 1 3 2

Requirements

• Python3.6.9
• Gurobi Optimizer version 9.0.2 is required to run NashMax and RevMax.

Results are saved in results/[algo_name]/ folder

Bibtex

@inproceedings{fairAllocInSocialCom,
  author = {Anjali Gupta and Shreyansh Nagori and Abhijnan Chakraborty and Rohit Vaish and Sayan Ranu and Prajit Nadkarni and Narendra Varma and Muthusamy Chelliah},
  title = {Towards Fair Allocation in Social Commerce Platforms},
  booktitle = {The Web Conference (formerly known as International World Wide Web Conference, abbreviated as WWW) },
  year = {2023}
}