Package recsys

Booking.com Challenge - WebTour 2021
ACM WSDM workshop

4th place solution for Booking.com's next destination recommendation challenge.

Documentation | Github | Colab

@inproceedings{DOI_CODE,
 author = {Baigorria Alonso, Martín},
 booktitle = {ACM WSDM Workshop on Web Tourism (WSDM WebTour’21)},
 howpublished = {\url{https://mbaigorria.github.io/booking-challenge-2021-recsys}},
 title = {Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems},
 year = {2021}
}

Model

My solution is a Sequence Aware Recommender System implemented with an RNN based Many-To-Many architecture.

Setting up resources

To set up the environment with all the datasets and resources, you must first call:

bash get_resources.sh

Installing and running package

You can install the package in development mode and get our submission.

pip install -e .
python3 -m recsys.src.submission

Running Docker image

You must first install nvidia-docker to run the Docker image with a GPU.

Then, install the base image we use:

docker pull nvidia/cuda

Finally, build and run our image:

docker build -t booking_challenge .
docker run --runtime=nvidia --ipc=host --mount source=${path_to_resources},target=/home/user/resources,type=bind recsys
Expand source code Browse git 
"""
.. include:: ../README.md
"""
import logging

from recsys.utils import check_device

logging.getLogger().setLevel(logging.INFO)

check_device()

Sub-modules

recsys.config
recsys.dataset
recsys.dataset_loader
recsys.encoders
recsys.experiments
recsys.model
recsys.model_selection
recsys.model_train
recsys.paths
recsys.plot
recsys.submission
recsys.types
recsys.utils