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Object org.apache.spark.mllib.recommendation.MatrixFactorizationModel
public class MatrixFactorizationModel
Model representing the result of matrix factorization.
Note: If you create the model directly using constructor, please be aware that fast prediction requires cached user/product features and their associated partitioners.
param: rank Rank for the features in this model. param: userFeatures RDD of tuples where each tuple represents the userId and the features computed for this user. param: productFeatures RDD of tuples where each tuple represents the productId and the features computed for this product.
Constructor Summary | |
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MatrixFactorizationModel(int rank,
RDD<scala.Tuple2<Object,double[]>> userFeatures,
RDD<scala.Tuple2<Object,double[]>> productFeatures)
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Method Summary | |
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static MatrixFactorizationModel |
load(SparkContext sc,
String path)
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double |
predict(int user,
int product)
Predict the rating of one user for one product. |
JavaRDD<Rating> |
predict(JavaPairRDD<Integer,Integer> usersProducts)
Java-friendly version of MatrixFactorizationModel.predict . |
RDD<Rating> |
predict(RDD<scala.Tuple2<Object,Object>> usersProducts)
Predict the rating of many users for many products. |
RDD<scala.Tuple2<Object,double[]>> |
productFeatures()
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int |
rank()
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Rating[] |
recommendProducts(int user,
int num)
Recommends products to a user. |
RDD<scala.Tuple2<Object,Rating[]>> |
recommendProductsForUsers(int num)
Recommends topK products for all users. |
Rating[] |
recommendUsers(int product,
int num)
Recommends users to a product. |
RDD<scala.Tuple2<Object,Rating[]>> |
recommendUsersForProducts(int num)
Recommends topK users for all products. |
void |
save(SparkContext sc,
String path)
Save this model to the given path. |
RDD<scala.Tuple2<Object,double[]>> |
userFeatures()
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Methods inherited from class Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.Logging |
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initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
Constructor Detail |
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public MatrixFactorizationModel(int rank, RDD<scala.Tuple2<Object,double[]>> userFeatures, RDD<scala.Tuple2<Object,double[]>> productFeatures)
Method Detail |
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public static MatrixFactorizationModel load(SparkContext sc, String path)
public int rank()
public RDD<scala.Tuple2<Object,double[]>> userFeatures()
public RDD<scala.Tuple2<Object,double[]>> productFeatures()
public double predict(int user, int product)
public RDD<Rating> predict(RDD<scala.Tuple2<Object,Object>> usersProducts)
usersProducts
- RDD of (user, product) pairs.
public JavaRDD<Rating> predict(JavaPairRDD<Integer,Integer> usersProducts)
MatrixFactorizationModel.predict
.
usersProducts
- (undocumented)
public Rating[] recommendProducts(int user, int num)
user
- the user to recommend products tonum
- how many products to return. The number returned may be less than this.
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.public Rating[] recommendUsers(int product, int num)
product
- the product to recommend users tonum
- how many users to return. The number returned may be less than this.
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.public void save(SparkContext sc, String path)
Saveable
This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/
The model may be loaded using Loader.load
.
save
in interface Saveable
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.public RDD<scala.Tuple2<Object,Rating[]>> recommendProductsForUsers(int num)
num
- how many products to return for every user.
public RDD<scala.Tuple2<Object,Rating[]>> recommendUsersForProducts(int num)
num
- how many users to return for every product.
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