Gatik Gola
Student, King's College, Taunton, UK
Download PDFRecommender systems have become ubiquitous in our lives. Yet, currently, they are far from optimal. In this project, we attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. We attempt to build a scalable model to perform this analysis. We start by preparing and comparing the various models on a smaller dataset of 100,000 ratings. Then we try to scale the algorithm so that it is able to handle 20 million ratings by using Apache Spark. We find that for the smaller dataset, using user-based collaborative filtering results in the Mean Squared Error on our dataset.
Keywords: movie recommendation system; User Based Collaborative Filtering; data scrapping
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