Distributed Movie Recommendation System with Hadoop

Recommender systems have become an essential part of digital platforms, helping users discover movies, books, and products they might like. However, building recommendation engines at scale requires efficient handling of massive amounts of user interaction data. Traditional single-node systems struggle with scalability. Hadoop’s distributed architecture makes it possible to process huge datasets using MapReduce and deliver movie recommendations even across millions of users and ratings.

By leveraging Hadoop MapReduce, you can distribute the computation of user similarities and item preferences across multiple nodes. Collaborative filtering methods like User-User or Item-Item similarity calculation are implemented in a distributed manner. You process rating datasets like MovieLens or Netflix Prize using MapReduce jobs to recommend movies to users based on their similarity with other users' viewing histories and preferences.

You start by preparing large user-movie rating datasets, such as MovieLens. Hadoop MapReduce jobs are designed to compute user similarities (cosine similarity or Pearson correlation) or item similarities based on rating patterns. After similarity scores are calculated, recommendations are generated by identifying top matches for each user. Final results are stored in HDFS and can be queried to deliver personalized movie suggestions for users.

• Ingest massive movie rating datasets into Hadoop Distributed File System (HDFS) for parallel processing.
• Use MapReduce programs to compute user-item interaction matrices, calculate similarity scores, and generate top recommendations.
• Optimize MapReduce job design for reducing shuffling and data transfer overheads across nodes.
• Store recommendation results back in HDFS or export them to a front-end system for visualization or user delivery.
• Deploy the entire system on a Hadoop cluster (local, pseudo-distributed, or on the cloud like AWS EMR) for scalability testing.

• MovieLens Dataset (for Recommendation Projects)
• Apache Hadoop Documentation
• Introduction to Recommendation Systems