Movie Recommendation System

Project Details

This project aims to develop a Movie Recommendation System that leverages the TMDB (The Movie Database) dataset to provide content-based recommendations.

Dataset

• TMDB: A rich dataset containing movie information, including titles, overviews, genres, and more.

Recommendation Type

• Content-based Recommendation: This system will recommend movies based on item similarity using the content of the movies.
• Other Types of Recommendations:
  • Item-based
  • User-based
  • Collaborative Filtering

Tech Stack to Use in Production

To build this system, we will utilize the following technologies:

• Data Storage and Processing:

  • Snowflake or Kafka: For managing and processing the TMDB dataset.

• Search and Recommendation Engine:

  • VESPA or ElasticSearch: For efficient indexing and searching of movie records.

• Workflow Management:

  • Apache Airflow: To orchestrate the data pipeline.
    • DAG 1: A Directed Acyclic Graph (DAG) to read the latest TMDB data into Snowflake, creating a TMDB table.
    • DAG 2: A DAG to process the TMDB table records and push the processed data to VESPA for further recommendations.

Getting Started

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>