Data Science Implementing Recommender System On Spark

In recent years, recommender systems have become an important part of online platforms. They help users find movies, books, products, and other content based on their preferences. Traditional collaborative filtering methods work well with smaller datasets, but their effectiveness usually drops as the dataset grows. This is where distributed computing platforms like Apache Spark can help. In this project, we expand the recommender system created in Project 4 by using a distributed environment with Apache Spark. We specifically employ Spark’s MLlib library and the sparklyr package in R to create a matrix factorization model with the Alternating Least Squares (ALS) algorithm. The main goal of this project is to see how a distributed computing method can effectively scale collaborative filtering techniques for larger datasets. Although the MovieLens 100k dataset used here is relatively small, it serves as a useful test case for getting familiar with the Spark ecosystem. We will learn how to prepare data, build models, and evaluate performance using distributed methods. We compare this Spark-based ALS model with earlier models in terms of how complex they are to implement, how well they perform, and how scalable they are. We also note potential benefits and drawbacks of using Spark and discuss when it’s necessary to transition from single-node to distributed environments. If you find this work useful, please cite it using the DOI: 10.5281/ZENODO.16377194