MatrixFactorizationModel

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

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16. final def notifyAll(): Unit

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17. def predict(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]]

    :: DeveloperApi :: Predict the rating of many users for many products.

    :: DeveloperApi :: Predict the rating of many users for many products. This is a Java stub for python predictAll()

    usersProductsJRDD

    A JavaRDD with serialized tuples (user, product)

    returns

    JavaRDD of serialized Rating objects.

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    @DeveloperApi()

18. 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.

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

    Predict the rating of one user for one product.

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

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

21. val rank: Int

    Rank for the features in this model.

22. 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.

23. 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.

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

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25. def toString(): String

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

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

27. final def wait(): Unit

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

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

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