server/src/main/resources/eclairjs/mllib/recommendation/MatrixFactorizationModel.js
function () {
        this.logger = Logger.getLogger("MatrixFactorizationModel_js");
        var jvmObject = arguments[0];
        if (arguments.length > 1) {
            var userFeatures = Utils.unwrapObject(arguments[1]);
            var productFeatures = Utils.unwrapObject(arguments[2]);
            jvmObject = new org.apache.spark.mllib.recommendation.MatrixFactorizationModel(arguments[0], userFeatures, productFeatures);
        }

        JavaWrapper.call(this, jvmObject);

    }
function () {
        this.logger = Logger.getLogger("MatrixFactorizationModel_js");
        var jvmObject = arguments[0];
        if (arguments.length > 1) {
            var userFeatures = Utils.unwrapObject(arguments[1]);
            var productFeatures = Utils.unwrapObject(arguments[2]);
            jvmObject = new org.apache.spark.mllib.recommendation.MatrixFactorizationModel(arguments[0], userFeatures, productFeatures);
        }

        JavaWrapper.call(this, jvmObject);

    }
function ( ) { this . logger = Logger . getLogger ( "MatrixFactorizationModel_js" ) ; var jvmObject = arguments [ 0 ] ; if ( arguments . length > 1 ) { var userFeatures = Utils . unwrapObject ( arguments [ 1 ] ) ; var productFeatures = Utils . unwrapObject ( arguments [ 2 ] ) ; jvmObject = new org . apache . spark . mllib . recommendation . MatrixFactorizationModel ( arguments [ 0 ] , userFeatures , productFeatures ) ; } JavaWrapper . call ( this , jvmObject ) ; }
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.
@classdesc
 
@param {number} rank
@param {module:eclairjs.RDD} userFeatures
@param {module:eclairjs.RDD} productFeatures
@class
@memberof module:eclairjs/mllib/recommendation
Model representing the result of matrix factorization .