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https://github.com/finn-no/recsys_slates_dataset

FINN.no Slate Dataset for Recommender Systems. A dataset containing all interactions (viewed items + response (clicked item / no click) for users over a longer time horizon.
https://github.com/finn-no/recsys_slates_dataset

dataset deep-learning pytorch recommender-system

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FINN.no Slate Dataset for Recommender Systems. A dataset containing all interactions (viewed items + response (clicked item / no click) for users over a longer time horizon.

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README 

        
FINN.no Slate Dataset for Recommender Systems

================



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%5Eu(s_t%5Eu)) (up to the

scroll length ![equ](https://latex.codecogs.com/gif.latex?s_t%5Eu)), and

the user’s click response

![equ](https://latex.codecogs.com/gif.latex?c_t%5Eu). 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:



``` python

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. 



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



## Todo



This repository is currently *work in progress*, and we will provide

descriptions and tutorials. Suggestions and contributions to make the

material more available are welcome. There are some features of the

repository that we are working on:



- [ ] Add more usable functions that compute relevant metrics such as

  F1, counterfactual metrics etc.

- [ ] The git lfs is currently broken by removing some lines in

  .gitattributes that is in conflict with nbdev. The dataset is still

  usable using the building download functions as they use a different

  source. However, we should fix this. An issue is [posted on

  nbdev](https://github.com/fastai/nbdev/issues/506).