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Building Recommendation Engines by Suresh Kumar Gorakala

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The recommendation engine approach

Now let's get into the actual implementation of the recommendation engine. We use the following approach to build the recommendation engine using Spark:

  1. Start the Spark environment.
  2. Load the data.
  3. Explore the data source.
  4. Use the MLlib recommendation engine module to generate the recommendations using ALS instance.
  5. Generate the recommendations.
  6. Evaluate the model.
  7. Using the cross_validation approach, apply the parameter tuning model to tune the parameter and select the best model, and then generate recommendations.

Implementation

Like any other recommendation engine, the first step is to load the data into the analytics environment (into the Spark environment in our case). When we start the Spark environment in the 2.0 ...

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