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Building Recommendation Engines by Suresh Kumar Gorakala

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Summary

In this chapter, we learned about model-based collaborative filtering using matrix factorization methods using ALS. We used the Python API to access the Spark framework and ran the ALS collaborative filtering. In the beginning of the chapter, we refreshed our knowledge of Spark with all the basics that are required to run the recommendation engines, such as what Spark is, the Spark ecosystem, components of Spark, SparkSession, DataFrames, RDD, and so on. As explained then, we explored the MovieLens data, built a basic recommendation engine, evaluated the model, and used parameter tuning to improve the model. In the next chapter, we shall learn about building recommendations using Graph database - Neo4j.

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