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c

scalation.analytics.recommender

UserBasedRecommender

class UserBasedRecommender extends Recommender

The UserBasedRecommender class is used to perform predictions based on User based Collaborative Filtering techniques (Cosine, Correlation, Adjusted Cosine)

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Instance Constructors

  1. new UserBasedRecommender(input: MatrixI, m: Int, n: Int)

    input

    original matrix

    m

    number of rows

    n

    number of columns

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
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    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. def colMeans: VectorD

    Create a vector of mean values of each column without including 0's

  7. def corrSim: Unit

    Generate the Correlation Similarity matrix

  8. def cosSim: Unit

    Generate the Cosine Similarity matrix

  9. def crossValidate(tester: MatrixD): Unit

    Phase 2: Cross validate the final predictions against the test dataset.

    Phase 2: Cross validate the final predictions against the test dataset.

    tester

    testing data matrix

    Definition Classes
    Recommender
  10. def denormalize(i: Int): Double

    Create a column denormalized version of 'this' matrix.

    Create a column denormalized version of 'this' matrix. return the denormalized value for the row index i

  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def error_metrics(input: MatrixI): Unit

    Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.

    Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.

    input

    the test portion of the original 4-column input matrix

    Definition Classes
    Recommender
  14. def genTrain2(train: MatrixI): Unit

    Generates the training MatrixD for the input dataset in the form MatrixI

    Generates the training MatrixD for the input dataset in the form MatrixI

    train

    : training data matrix

  15. def genTrainTest(exl: VectorI, input: MatrixI): MatrixD

    Generates the training matrix for the dataset

    Generates the training matrix for the dataset

    exl

    : vector of index values which will be excluded in the train

    input

    : original data matrix

  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  17. def getStats: Array[Statistic]

    Return the variables for the statistics vectors.

    Return the variables for the statistics vectors.

    Definition Classes
    Recommender
  18. def hashCode(): Int
    Definition Classes
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    Annotations
    @native() @HotSpotIntrinsicCandidate()
  19. def impute(training: MatrixD): MatrixD

    Column mean Imputation of null values in the input matrix

  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. def makeRatings(input: MatrixI, m: Int, n: Int): MatrixD

    Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns.

    Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns. The 'input' matrix has type MatrixI, while the 'ratings' matrix has type MatrixD.

    input

    the original 4-column input data matrix containing ratings, e.g., from a file

    m

    the number of rows for the ratings matrix

    n

    the number of columns for the ratings matrix

    Definition Classes
    Recommender
  22. def minNZ: Double

    Return the minimum non-zero element of the entire matrix

  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. def normalize: Unit

    Create a column normalized version of 'this' matrix.

    Create a column normalized version of 'this' matrix. for all values that are not 0 replace with self - row mean (mean calculation doesnot include 0s)

  25. final def notify(): Unit
    Definition Classes
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    Annotations
    @native() @HotSpotIntrinsicCandidate()
  26. final def notifyAll(): Unit
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  27. def rate(i: Int, j: Int): Double

    Generate a rating based on coorelation similarity between n similar users

    Generate a rating based on coorelation similarity between n similar users

    i

    user

    j

    item

    Definition Classes
    UserBasedRecommender → Recommender
  28. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  29. def test(istart: Int, iend: Int, input: MatrixI): Unit

    Phase 3: Test the accuracy of the predictions and add it to the statistics vector.

    Phase 3: Test the accuracy of the predictions and add it to the statistics vector.

    istart

    the start point

    iend

    the end point

    input

    the original 4-column input matrix

    Definition Classes
    Recommender
  30. def toString(): String
    Definition Classes
    AnyRef → Any
  31. def topk(x: VectorD, k: Int): VectorI

    Return the indices of the 'k' largest values in vector 'x'.

    Return the indices of the 'k' largest values in vector 'x'. FIX - replace with more efficient top-k algorithm

    x

    the input vector

    k

    the number of values to be returned

    Definition Classes
    Recommender
  32. def topk2(x: VectorD, k: Int): Array[Int]

    Return the indices of the 'k' largest values in vector 'x'.

    Return the indices of the 'k' largest values in vector 'x'.

    x

    the input vector

    k

    the number of values to be returned

    Definition Classes
    Recommender
  33. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    Annotations
    @throws( ... )
  34. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws( ... ) @native()
  35. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  36. def zRatings(limit: Int, input: MatrixI): Unit

    Returns a matrix with replacing all values with 0 beyond the interval specified

    Returns a matrix with replacing all values with 0 beyond the interval specified

    limit

    : interval start point

    input

    : original data matrix

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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