FINN.no Recommender Systems Slate Dataset
This repository accompany the paper "Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling" by Simen Eide, David S. Leslie and Arnoldo Frigessi. The article is under review, and the pre-print can be obtained here.
The repository is split into the dataset (data/) and the accompanying code for the paper (code/).
We release the FINN.no recommender systems slate dataset to improve recommender systems research. The dataset includes both search and recommendation interactions between users and the platform over a 30 day period. The dataset has logged both exposures and clicks, including interactions where the user did not click on any of the items in the slate.
For each user u and interaction step t we recorded all items in the visible slate (up to the scroll length
), and the user's click response
. The dataset consists of 37.4 million interactions, |U| ≈ 2.3) million users and |I| ≈ 1.3 million items that belong to one of G = 290 item groups. For a detailed description of the data please see the paper.
FINN.no is the leading marketplace in the Norwegian classifieds market and provides users with a platform to buy and sell general merchandise, cars, real estate, as well as house rentals and job offerings.
This repository is currently work in progress, and we will provide descriptions and tutorials. Suggestions and contributions to make the material more available is welcome.
For questions, email [email protected] or file an issue.
Download and prepare dataset
The data file is compressed, unzip by the following command: gunzip -c data.pt.gz >data.pt
- Install git-lfs: This repository uses
git-lfsto store the dataset. Therefore you need the git-lfs package in addition to github. See [https://git-lfs.github.com/] for installation instructions. (e.g. for apt-getsudo apt-get install git-lfs) - Clone the repository
- The data file is compressed, unzip by the following command:
gunzip -c data.pt.gz >data.pt
Quickstart dataset 
We provide a quickstart jupyter notebook that runs on Google Colab (quickstart-finn-recsys-slate-data.ipynb) which includes all necessary steps above.
Citations
If you use either the code, data or paper, please consider citing the paper.
@article{eide2021dynamic,
title={Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling},
author={Simen Eide and David S. Leslie and Arnoldo Frigessi},
year={2021},
eprint={2104.15046},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
Todo
There are some limitations on the repository today that we would like to improve:
- Release the dataset as numpy objects instead of pytorch arrays. This will help non-pytorch users to more easily utilize the data
- Maintain a pytorch dataset for easy usage
- Create a pip package for easier installation and usage. the package should download the dataset using a function.
- Add easily useable functions that compute relevant metrics such as hitrate, log-likelihood etc.
- Distribute the data on other platforms such as kaggle.
- Add a short description of the data in the readme.md directly.
As the current state is in early stage, it makes sense to allow the above changes non-backward compatible. However, this should be completed within the next couple of months.
![[WIP] Add Short Description Data](https://avatars.githubusercontent.com/u/36044574?v=4)
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