MatrixFactorizationModel

Instance Constructors

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

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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val formatVersion: String

Current version of model save/load format.
Current version of model save/load format.

Attributes
protected
Definition Classes
MatrixFactorizationModel → Saveable
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(usersProducts: JavaPairRDD[Integer, Integer]): JavaRDD[Rating]

Java-friendly version of MatrixFactorizationModel.predict.
Java-friendly version of MatrixFactorizationModel.predict.

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

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" )
def predict(user: Int, product: Int): Double

Predict the rating of one user for one product.
Predict the rating of one user for one product.

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

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" )
val rank: Int

Rank for the features in this model.
Rank for the features in this model.

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

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" )
def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])]

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" )
def recommendUsers(product: Int, num: Int): Array[Rating]

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" )
def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])]

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" )
def save(sc: SparkContext, path: String): Unit

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
MatrixFactorizationModel → Saveable
Annotations
@Since( "1.3.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
val userFeatures: RDD[(Int, Array[Double])]

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" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object MatrixFactorizationModel | package recommendation

class MatrixFactorizationModel extends Saveable with Serializable with Logging

Instance Constructors

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

val formatVersion: String

final def getClass(): Class[_]

def hashCode(): Int

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

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

def logDebug(msg: ⇒ String): Unit

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

def logError(msg: ⇒ String): Unit

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

def logInfo(msg: ⇒ String): Unit

def logName: String

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

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating]

def predict(user: Int, product: Int): Double

val productFeatures: RDD[(Int, Array[Double])]

val rank: Int

def recommendProducts(user: Int, num: Int): Array[Rating]

def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])]

def recommendUsers(product: Int, num: Int): Array[Rating]

def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])]

def save(sc: SparkContext, path: String): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

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

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

Ungrouped