diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java index 81970b7c81f406bd48a77fbc333e533396aad604..60ef03d89d17b1b171e516999d6d2449083ae5e0 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java @@ -113,7 +113,15 @@ public class JavaALSExample { .setPredictionCol("prediction"); Double rmse = evaluator.evaluate(predictions); System.out.println("Root-mean-square error = " + rmse); + + // Generate top 10 movie recommendations for each user + Dataset<Row> userRecs = model.recommendForAllUsers(10); + // Generate top 10 user recommendations for each movie + Dataset<Row> movieRecs = model.recommendForAllItems(10); // $example off$ + userRecs.show(); + movieRecs.show(); + spark.stop(); } } diff --git a/examples/src/main/python/ml/als_example.py b/examples/src/main/python/ml/als_example.py index 2e7214ed56f980ec8c931dc9bc287ff05a802b89..1672d552eb1d5e6101ed47368c23378550e79e5e 100644 --- a/examples/src/main/python/ml/als_example.py +++ b/examples/src/main/python/ml/als_example.py @@ -55,5 +55,13 @@ if __name__ == "__main__": predictionCol="prediction") rmse = evaluator.evaluate(predictions) print("Root-mean-square error = " + str(rmse)) + + # Generate top 10 movie recommendations for each user + userRecs = model.recommendForAllUsers(10) + # Generate top 10 user recommendations for each movie + movieRecs = model.recommendForAllItems(10) # $example off$ + userRecs.show() + movieRecs.show() + spark.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala index 868f49b16f2187e9453714998b86c319a5b219e4..07b15dfa178f7c3170e27b5e569e74035f1b9139 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala @@ -75,7 +75,14 @@ object ALSExample { .setPredictionCol("prediction") val rmse = evaluator.evaluate(predictions) println(s"Root-mean-square error = $rmse") + + // Generate top 10 movie recommendations for each user + val userRecs = model.recommendForAllUsers(10) + // Generate top 10 user recommendations for each movie + val movieRecs = model.recommendForAllItems(10) // $example off$ + userRecs.show() + movieRecs.show() spark.stop() }