Question

A media streaming company wants to build a recommendation engine that suggests movies to users. The only data available is a log of which movies each user has watched (there are no explicit star ratings, likes, or dislikes). The team has hundreds of thousands of users and tens of thousands of titles. Which modeling approach is MOST appropriate for generating personalized recommendations from this data?

Accepted Answer

Train a matrix factorization / collaborative filtering model that treats the watch events as implicit feedback signals

Answer

Train a supervised regression model to predict a numeric rating for each user-movie pair

Answer

Use a k-means clustering model to group movies and recommend the largest cluster to every user

Answer

Train a binary classifier that outputs whether a movie is globally popular and recommend the top predictions to all users