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Diffstat (limited to 'docs/mllib-collaborative-filtering.md')
| -rw-r--r-- | docs/mllib-collaborative-filtering.md | 6 |
1 files changed, 6 insertions, 0 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index eedc23424a..b3fd51dca5 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -64,6 +64,8 @@ We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.rec method which assumes ratings are explicit. We evaluate the recommendation model by measuring the Mean Squared Error of rating prediction. +Refer to the [`ALS` Scala docs](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.recommendation.ALS import org.apache.spark.mllib.recommendation.MatrixFactorizationModel @@ -119,6 +121,8 @@ Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a calling `.rdd()` on your `JavaRDD` object. A self-contained application example that is equivalent to the provided example in Scala is given bellow: +Refer to the [`ALS` Java docs](api/java/org/apache/spark/mllib/recommendation/ALS.html) for details on the API. + {% highlight java %} import scala.Tuple2; @@ -201,6 +205,8 @@ In the following example we load rating data. Each row consists of a user, a pro We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation by measuring the Mean Squared Error of rating prediction. +Refer to the [`ALS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.recommendation.ALS) for more details on the API. + {% highlight python %} from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating |