Build a Movie Recommendation App with OpenAI
Offered By: Scrimba via Coursera
Course Description
Overview
In this project, you'll develop Pop Choice, an AI-powered movie recommendation app designed to simplify the decision-making process for movie night.
Using AI embeddings, vector databases, and the OpenAI API, the app will recommend the perfect movie based on user preferences gathered through a series of open and close-ended questions. Users input their mood and preferences, and the app searches a database of movies to find the best match.
As a stretch goal, the app can be adapted for group recommendations, with each participant's preferences being taken into account. You'll build this project from scratch, using any framework you prefer, like React or vanilla JavaScript, and integrate a vector database, such as Supabase. The provided data includes movie details like title, plot, cast, and IMDb ratings.
This project emphasizes working with embeddings, querying databases, and generating personalized outputs using AI.
Syllabus
- Project Overview
- Develop Pop Choice, an AI-powered movie recommendation app that uses user input to generate personalized movie suggestions for both individuals and groups.
Taught by
Rafid Hoda
Related Courses
Vector Similarity SearchData Science Dojo via YouTube Supercharging Semantic Search with Pinecone and Cohere
Pinecone via YouTube Search Like You Mean It - Semantic Search with NLP and a Vector Database
Pinecone via YouTube The Rise of Vector Data
Pinecone via YouTube NER Powered Semantic Search in Python
James Briggs via YouTube