Package: recsys-slates-dataset
FINN.no Slate Dataset for Recommender SystemsWe release theFINN.no slate datasetto 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.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 thepaper.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, emailsimen.eide@finn.noor file an issue.Installpip install recsys_slates_datasetHow to useTo download the generic numpy data files:fromrecsys_slates_datasetimportdata_helperdata_helper.download_data_files(data_dir="data")Download and prepare data into ready-to-use PyTorch dataloaders:fromrecsys_slates_datasetimportdataset_torchind2val,itemattr,dataloaders=dataset_torch.load_dataloaders(data_dir="data")OrganizationThe repository is organized as follows: - The dataset is placed indata/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”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 usethe v1.0 release of the
repositoryfor a compatible version of the code and dataset.Quickstart datasetWe 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 scriptsWe provide an example training jupyter notebook that implements a matrix
factorization model with categorical loss that can be found inexamples/. It is also runnable using Google Colab:There is ongoing work in progress to build additional examples and use
them as benchmarks for the dataset.Dataset filesThe datasetdata.npzcontains 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 datasetitemattr.npzcontains 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 datasetind2val.jsoncontains 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”by Simen Eide, David S.
Leslie and Arnoldo Frigessi. The article is under review, and the
preprint can be obtainedhere.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-wTodoThis repository is currentlywork 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 isposted on
nbdev.