Carnegie Mellon University

Integrated Innovation Institute

Engineering + Design + Business

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How can we personalize movie recommendations?

Researching movie recommendations can be frustrating: social media is often flooded with conflicting opinions and we don’t always share tastes with film critics. With so much varying information, movie-goers often struggle to determine which movie to see.

The Solution

Based on watch history, the latest releases, and location settings, What2Watch personalizes movie recommendations to match user preferences. What2Watch utilizes two primary inputs: the user’s three favorite movies and characters. Once the user inputs their favorites into the W2W app, the app suggests a movie, along with a description, trailer, and other user reviews. Based on location settings, W2W will suggest movies that are playing in nearby theaters.

The student team used machine learning algorithms to build What2Watch. The content-based algorithm searches a movie database and determines relevant matches based on user preferences. The collaborative filtering algorithm collects and analyzes an extensive amount of information based on user behaviors, and creates recommendations based on inputs similar to other users.


What2Watch Screenshots
What2Watch App's User Interface