Each user is potentially interested in watching one or more of the movies specified in requests.json
. Our job is to decide which movie or movies to recommend to these users.
Use a combination of matrix factorization model (ALS) and a cold start model (using user and movie metadata) to fill in the NaN values and predict ratings for these movies.
Your predictions will be scored as follows:
- Each user may watch movies from your list, starting with the highest predicted rating.
- Your model will be scored based on how well the users liked the movies they watched.
Minimal requirements:
- You must replace each NA value with a prediction.
- You must create both a matrix factorization and a cold start model.
- You team must document its work in a GitHub repo.
- Your repo must include multiple commits from each team member.