org.apache.spark.mllib.recommendation
MatrixFactorizationModel
Companion object MatrixFactorizationModel
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.
- Alphabetic
- By Inheritance
- MatrixFactorizationModel
- Logging
- Serializable
- Serializable
- Saveable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
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
- @native() @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
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
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
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
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])]
- Annotations
- @Since( "0.8.0" )
-
val
rank: Int
- 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])]
- 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
- @native() @throws( ... )