Movie Recommendation App built in NextJS

Debo Adebayo

A movie recommendation app leverages NextJS for a fast, server-side rendered frontend experience. At its core, the app utilises a vector database to store and efficiently query movie plot summaries as high-dimensional vectors. Similar to companies which use recommendation engines such as Amazon for products or Netflix for movies.
Key features:
User Interface: Clean, responsive design built with NextJS components for seamless navigation and movie selection.
Vector Embedding: Movie plot summaries are converted into numerical vectors using natural language processing techniques. Leveraged open AI's model: text-embedding-3-large.
Vector Database: Employs a specialised database (AstraDB) for storing and querying high-dimensional vectors. Integration with Open AI
Cosine Similarity: When a user selects a movie, the app calculates cosine similarity between the chosen movie's vector and all other movies in the database.
Recommendation Engine: Returns a list of movies with the highest cosine similarity scores, representing the most similar plot lines.
Performance Optimization: NextJS's server-side rendering and automatic code splitting ensure fast load times and a smooth user experience.
Comparison Page
This app provides users with an engaging way to discover new movies based on plot similarities to their favourites, offering a cool approach to content recommendation in the streaming era based on the latest techniques.
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Posted Aug 14, 2024

App built in NextJS utilising a vectorDB and cosine similarity of vector embeddings to recommend movies with similar plots. Hosted in vercel

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