This project intention is to explore different common recommendation strategies.
The definitions presented in this report are a compilation and reinterpretation sourced from various references, each attributed in my best capacity. It is important to clarify that I did not develop nor contribute to the creation of any of the algorithms discussed. Rather, I have packaged them into a standardized interface for the purpose of testing and exploring different recommendation systems with modifications to suit the testing and implementation frameworks.
Download also the proper libraries from requirements.txt and spacy models:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt --upgrade
python -m spacy download en_core_web_sm
Running Locally (Streamlit):
cd streamlit
# UNZIP data.zip inside of streamlit folder
python model_makers.py
streamlit run main.py
Make sure you populate the models folders:
- Streamlit: Application
- Custom Recommneder Engine: https://github.com/NeneWang/recommendation-systems-exploration/blob/master/streamlit/customrec_engine.py
- Contains all the code for the recommender engines used on the Streamlit Application
- Enhancement: all Models tests and recommendation engiens tests | Here is where the written report focuses on.
- Custom Recommender Engine: https://github.com/NeneWang/recommendation-systems-exploration/blob/master/enhancement/customrec_engine.py
- Constains all the code for the recommenders
If deploying on streamlit, I using instead:
engines_list_streamlit
engines_streamlit
Please download the books and datasets from the following links:
https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset?resource=download
Extract them into the data folder
git pull -X theirs
- Enhancement: Exploration into the different types of recommendation systems. And Concrete implementation on the datasets we will be handling.
Some of the code were taken from the following guides, I included code that I used for testing the models.
- Hands-On Recommendation Systems with Python
- Video Based: Recommender Systems with Machine Learning
- Recommender Systems Complete
| Date | Details |
|---|---|
| 2024-03-28 18:15:57 | Finished Reading Hands On Recommendation |