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README.md

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+# FINN.no Slate Dataset for Recommender Systems
+> Data and helper functions for FINN.no slate dataset containing both viewed items and clicks from the FINN.no second hand marketplace.
+
+Note: The dataset is originally hosted at https://github.com/finn-no/recsys_slates_dataset and this is a copy of the readme until this repo is properly created "huggingface-style".
+
+We release the *FINN.no 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*.
+To our knowledge there exists no such large-scale dataset, and we hope this contribution can help researchers constructing improved models and improve offline evaluation metrics.
+
+![A visualization of a presented slate to the user on the frontpage of FINN.no](finn-frontpage.png)
+
+For each user u and interaction step t we recorded all items in the visible slate ![equ](https://latex.codecogs.com/gif.latex?a_t^u(s_t^u) ) (up to the scroll length ![equ](https://latex.codecogs.com/gif.latex?s_t^u)), and the user's click response ![equ](https://latex.codecogs.com/gif.latex?c_t^u).
+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](https://arxiv.org/abs/2104.15046).
+
+![A visualization of a presented slate to the user on the frontpage of FINN.no](interaction_illustration.png)
+
+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.
+For questions, email [email protected] or file an issue.
+
+## Install
+
+`pip install recsys_slates_dataset`
+
+## How to use
+
+To download the generic numpy data files:
+
+```
+from recsys_slates_dataset import data_helper
+data_helper.download_data_files(data_dir="data")
+```
+
+Download and prepare data into ready-to-use PyTorch dataloaders:
+
+``` python
+from recsys_slates_dataset import dataset_torch
+ind2val, itemattr, dataloaders = dataset_torch.load_dataloaders(data_dir="data")
+```
+
+## Organization
+The repository is organized as follows:
+- The dataset is placed in `data/` and stored using git-lfs. We also provide an automatic download function in the pip package (preferred usage).
+- The code open sourced from the article ["Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling"](https://arxiv.org/abs/2104.15046) is found in (`code_eide_et_al21/`). However, we are in the process of making the data more generally available which makes the code incompatible with the current (newer) version of the data. Please use [the v1.0 release of the repository](https://github.com/finn-no/recsys-slates-dataset/tree/v1.0) for a compatible version of the code and dataset.
+
+## Quickstart dataset [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/finn-no/recsys-slates-dataset/blob/main/examples/quickstart-finn-recsys-slate-data.ipynb)
+We provide a quickstart Jupyter notebook that runs on Google Colab (quickstart-finn-recsys-slate-data.ipynb) which includes all necessary steps above.
+It gives a quick introduction to how to use the dataset.
+
+## Example training scripts
+We provide an example training jupyter notebook that implements a matrix factorization model with categorical loss that can be found in `examples/`.
+It is also runnable using Google Colab: [![matrix_factorization.ipynb](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/finn-no/recsys-slates-dataset/blob/main/examples/matrix_factorization.ipynb)  
+There is ongoing work in progress to build additional examples and use them as benchmarks for the dataset.
+
+### Dataset files
+The dataset `data.npz` contains the following fields:
+- userId: The unique identifier of the user.
+- click: The items the user clicked on in each of the 20 presented slates.
+- click_idx: The index the clicked item was on in each of the 20 presented slates.
+- slate_lengths: The length of the 20 presented slates.
+- slate: All the items in each of the 20 presented slates.
+- interaction_type: The recommendation slate can be the result of a search query (1), a recommendation (2) or can be undefined (0).
+
+The dataset `itemattr.npz` contains the categories ranging from 0 to 290. Corresponding with the 290 unique groups that the items belong to. These 290 unique groups are constructed using a combination of categorical information and the geographical location. 
+
+The dataset  `ind2val.json` contains the mapping between the indices and the values of the categories (e.g. `"287": "JOB, Rogaland"`) and interaction types (e.g. `"1": "search"`).                                                                                                                                                                  
+## Citations
+This repository accompanies the paper ["Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling"](https://arxiv.org/abs/2104.15046) by Simen Eide, David S. Leslie and Arnoldo Frigessi.
+The article is under review, and the preprint can be obtained [here](https://arxiv.org/abs/2104.15046).
+
+If you use either the code, data or paper, please consider citing the paper.
+
+```
+Eide, S., Leslie, D.S. & Frigessi, A. Dynamic slate recommendation with gated recurrent units and Thompson sampling. Data Min Knowl Disc (2022). https://doi.org/10.1007/s10618-022-00849-w
+```
+
 ---
 license: apache-2.0
 ---