In today’s technology driven world, recommender systems are socially and economically critical to ensure that individuals can make optimised choices surrounding the content they engage with on a daily basis. One application where this is especially true is movie recommendations; where intelligent algorithms can help viewers find great titles from tens of thousands of options.
With this context, EDSA is challenging you to construct a recommendation algorithm based on content or collaborative filtering, capable of accurately predicting how a user will rate a movie they have not yet viewed, based on their historical preferences.
Providing an accurate and robust solution to this challenge has immense economic potential, with users of the system being personalised recommendations - generating platform affinity for the streaming services which best facilitates their audience's viewing.
In this light, We as Team JM2 have been tasked with a responsibility to develope a movie recommendation system.
Here is a breakdown on how we worked on this project
The data for the MovieLens dataset is maintained by the GroupLens research group in the Department of Computer Science and Engineering at the University of Minnesota. Additional movie content data was legally scraped from IMDB
genome_scores.csv - a score mapping the strength between movies and tag-related properties. Read more here
genome_tags.csv - user assigned tags for genome-related scores
imdb_data.csv - Additional movie metadata scraped from IMDB using the links.csv file.
links.csv - File providing a mapping between a MovieLens ID and associated IMDB and TMDB IDs.
sample_submission.csv - Sample of the submission format for the hackathon.
tags.csv - User assigned for the movies within the dataset.
test.csv - The test split of the dataset. Contains user and movie IDs with no rating data.
train.csv - The training split of the dataset. Contains user and movie IDs with associated rating data.
Python
Commet
Steamlit
Anaconda
Contributions are always welcome!
See contributing.md for ways to get started.
. Kelvin Mwaniki
. Kusanele Mpofu
. Thapelo Mofokeng
. Oludare Adekunle
. Jeff Ouma