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An item-based recommender model that computes cosine similarity for each item pairs using the item factors matrix generated by Spark MLlib’s ALS algorithm and recommends top 5 items based on the selected item.
Launched a distributed application using Spark and MLlib ALS recommendation engine to analyze a complex dataset of 10 million movie ratings from MovieLens.
📊 📑This project provides a step-by-step big data analytics applied in the retail industry through the use of a variety of big data technologies. such as HDFS, Hive and Spark..