Visit: https://movie-night-recommendation.herokuapp.com
1. Note that it might take some time for the Heroku application to load, please be patient.
2. Note that it takes approximately 50 seconds for the recommendations to be returned.
git clone https://github.com/GroupMovieRec/group-movie-recommendation.git
cd group-movie-recommendation
python app.py
- Open browser at the respective address. (Chrome or Firefox recommended)
For example, that address might look something like:http://0.0.0.0:5000/
.
In the testing/
folder you'll be able to find all necessary files in order to run the tests for comparing our method's performance.
testing_group_differences.py
andtesting_method_comparison.py
importnmf.py
,rdfnmf.py
, andrdfnmf_updated.py
.nmf.py
is by created by Hung-Hsuan Chen [email protected].rdfnmf.py
andrdfnmf_updated.py
are heavily modified by us, but are based on Hung-Hsuan Chen's work.- In the testing files you can change the 50 iterations to something less. When you create the models by initialising and declaring a class for the different methods you can also reset the n_epochs to something less than 50.
- We would like to thank the authors of Movinder for publishing the code for their application, as a part of that has been the baseline for our user-interface. If you’re curious about the Movinder project, make sure to check them out their GitHub repository.
- We would also like the thank Chen & Chen for publishing their code for the RDFNMF, as this was the baseline for our weighted matrix factorisation implementation. If you are interest in there work, check out their GitHub repository for their paper.
The report for this project can be found here.
Group project created in the context of TU Delft's CS4065 Multimedia Search and Recommendation.
Team 8:
- Shreyan Biswas
- Caroline Freyer
- Francesca Drummer
- Stefan Petrescu
Video presentation can be watched here.