| # 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. | |
|  | |
| 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](https://arxiv.org/abs/2104.15046). | |
|  | |
| 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 [](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: [](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 | |
| --- | |