Class/Object

org.apache.spark.mllib.recommendation

MatrixFactorizationModel

Related Docs: object MatrixFactorizationModel | package recommendation

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class MatrixFactorizationModel extends Saveable with Serializable with Logging

Model representing the result of matrix factorization.

Annotations
@Since( "0.8.0" )
Note

If you create the model directly using constructor, please be aware that fast prediction requires cached user/product features and their associated partitioners.

Linear Supertypes
Logging, Serializable, Serializable, Saveable, AnyRef, Any
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  1. MatrixFactorizationModel
  2. Logging
  3. Serializable
  4. Serializable
  5. Saveable
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Instance Constructors

  1. new MatrixFactorizationModel(rank: Int, userFeatures: RDD[(Int, Array[Double])], productFeatures: RDD[(Int, Array[Double])])

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    rank

    Rank for the features in this model.

    userFeatures

    RDD of tuples where each tuple represents the userId and the features computed for this user.

    productFeatures

    RDD of tuples where each tuple represents the productId and the features computed for this product.

    Annotations
    @Since( "0.8.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  9. val formatVersion: String

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    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    MatrixFactorizationModelSaveable
  10. final def getClass(): Class[_]

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    Definition Classes
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  11. def hashCode(): Int

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  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
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    Logging
  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
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    Logging
  14. final def isInstanceOf[T0]: Boolean

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  15. def isTraceEnabled(): Boolean

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    protected
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    Logging
  16. def log: Logger

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    Logging
  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  18. def logDebug(msg: ⇒ String): Unit

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    Logging
  19. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  20. def logError(msg: ⇒ String): Unit

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    Logging
  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  22. def logInfo(msg: ⇒ String): Unit

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    Logging
  23. def logName: String

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    Logging
  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  25. def logTrace(msg: ⇒ String): Unit

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    protected
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    Logging
  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  27. def logWarning(msg: ⇒ String): Unit

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    Logging
  28. final def ne(arg0: AnyRef): Boolean

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  29. final def notify(): Unit

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    AnyRef
  30. final def notifyAll(): Unit

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    AnyRef
  31. def predict(usersProducts: JavaPairRDD[Integer, Integer]): JavaRDD[Rating]

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    Java-friendly version of MatrixFactorizationModel.predict.

    Java-friendly version of MatrixFactorizationModel.predict.

    Annotations
    @Since( "1.2.0" )
  32. def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating]

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    Predict the rating of many users for many products.

    Predict the rating of many users for many products. The output RDD has an element per each element in the input RDD (including all duplicates) unless a user or product is missing in the training set.

    usersProducts

    RDD of (user, product) pairs.

    returns

    RDD of Ratings.

    Annotations
    @Since( "0.9.0" )
  33. def predict(user: Int, product: Int): Double

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    Predict the rating of one user for one product.

    Predict the rating of one user for one product.

    Annotations
    @Since( "0.8.0" )
  34. val productFeatures: RDD[(Int, Array[Double])]

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    RDD of tuples where each tuple represents the productId and the features computed for this product.

    RDD of tuples where each tuple represents the productId and the features computed for this product.

    Annotations
    @Since( "0.8.0" )
  35. val rank: Int

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    Rank for the features in this model.

    Rank for the features in this model.

    Annotations
    @Since( "0.8.0" )
  36. def recommendProducts(user: Int, num: Int): Array[Rating]

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    Recommends products to a user.

    Recommends products to a user.

    user

    the user to recommend products to

    num

    how many products to return. The number returned may be less than this.

    returns

    Rating objects, each of which contains the given user ID, a product ID, and a "score" in the rating field. Each represents one recommended product, and they are sorted by score, decreasing. The first returned is the one predicted to be most strongly recommended to the user. The score is an opaque value that indicates how strongly recommended the product is.

    Annotations
    @Since( "1.1.0" )
  37. def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])]

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    Recommends top products for all users.

    Recommends top products for all users.

    num

    how many products to return for every user.

    returns

    [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of rating objects which contains the same userId, recommended productID and a "score" in the rating field. Semantics of score is same as recommendProducts API

    Annotations
    @Since( "1.4.0" )
  38. def recommendUsers(product: Int, num: Int): Array[Rating]

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    Recommends users to a product.

    Recommends users to a product. That is, this returns users who are most likely to be interested in a product.

    product

    the product to recommend users to

    num

    how many users to return. The number returned may be less than this.

    returns

    Rating objects, each of which contains a user ID, the given product ID, and a "score" in the rating field. Each represents one recommended user, and they are sorted by score, decreasing. The first returned is the one predicted to be most strongly recommended to the product. The score is an opaque value that indicates how strongly recommended the user is.

    Annotations
    @Since( "1.1.0" )
  39. def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])]

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    Recommends top users for all products.

    Recommends top users for all products.

    num

    how many users to return for every product.

    returns

    [(Int, Array[Rating])] objects, where every tuple contains a productID and an array of rating objects which contains the recommended userId, same productID and a "score" in the rating field. Semantics of score is same as recommendUsers API

    Annotations
    @Since( "1.4.0" )
  40. def save(sc: SparkContext, path: String): Unit

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    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    MatrixFactorizationModelSaveable
    Annotations
    @Since( "1.3.0" )
  41. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
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  42. def toString(): String

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  43. val userFeatures: RDD[(Int, Array[Double])]

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    RDD of tuples where each tuple represents the userId and the features computed for this user.

    RDD of tuples where each tuple represents the userId and the features computed for this user.

    Annotations
    @Since( "0.8.0" )
  44. final def wait(): Unit

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    @throws( ... )
  45. final def wait(arg0: Long, arg1: Int): Unit

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  46. final def wait(arg0: Long): Unit

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Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

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