Recommender system for the hotel reservation. The goal of this project was to achieve the best HR@10 score in the final evaluation.
Author: Jakub Fiturski
Link to project
Project contains two Jupyter notebooks. First one project_1_data_preparation.ipynb which contains data preparation. Second one project_1_recommender_and_evaluation.ipynb which contains ContentBasedUserItemRecommender body and tuning methods.
Project also contains data preprocessing files:
- data_preprocessing/data_preprocessing_toolkit.py
- data_preprocessing/dataset_specification.py
- data_preprocessing/people_identifier.py
In the project data/hotel_data/hotel_data_original.csv file was used as data source.
Also, there are generated HTML files containing the results of running the code.
The aim of the project is to create hotel room recommender using given dataset which returns the best result in HR@10. Created recommender is tuned and evaluated. Then the results are compared to those given by Amazon recommender.
Project is created with:
To run the code on your local machine, you must install the following libraries:
- Anaconda with Python 3.8
- Dependencies listed in the
environment.ymlfile
Then go to your project directory. The best way to prepare the environment is use:
$ conda env create --name your-env-name -f environment.yml
$ conda activate your-env-name
Finally, in order to open the project use:
$ jupyter notebook
Now everything is prepared. Use notebooks in the given order:
- project_1_data_preparation.ipynb
- project_1_recommender_and_evaluation.ipynb
