Software details for the use case are as follows:
Start the Spark session using pyspark, as follows:
The following screenshot shows the Spark session created by running the above
To build the recommendation engine using Spark, we make use of Spark 2.0 capabilities, such as DataFrames, RDD, Pipelines, and Transforms available in Spark MLlib, which has was explained earlier.
Unlike earlier heurist approaches, such as k-nearest neighboring approaches used for building recommendation engines, in Spark, matrix factorization methods are used ...