server/src/main/resources/eclairjs/mllib/recommendation/ALS.js
function (jvmObject) {

        this.logger = Logger.getLogger("mllib_recommendation_ALS_js");
        if (!jvmObject) {
            jvmObject = new org.apache.spark.mllib.recommendation.ALS();
        }
        JavaWrapper.call(this, jvmObject);

    }
function (jvmObject) {

        this.logger = Logger.getLogger("mllib_recommendation_ALS_js");
        if (!jvmObject) {
            jvmObject = new org.apache.spark.mllib.recommendation.ALS();
        }
        JavaWrapper.call(this, jvmObject);

    }
function ( jvmObject ) { this . logger = Logger . getLogger ( "mllib_recommendation_ALS_js" ) ; if ( ! jvmObject ) { jvmObject = new org . apache . spark . mllib . recommendation . ALS ( ) ; } JavaWrapper . call ( this , jvmObject ) ; }
Alternating Least Squares matrix factorization.

ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices,
`X` and `Y`, i.e. `X * Yt = R`. Typically these approximations are called 'factor' matrices.
The general approach is iterative. During each iteration, one of the factor matrices is held
constant, while the other is solved for using least squares. The newly-solved factor matrix is
then held constant while solving for the other factor matrix.

This is a blocked implementation of the ALS factorization algorithm that groups the two sets
of factors (referred to as "users" and "products") into blocks and reduces communication by only
sending one copy of each user vector to each product block on each iteration, and only for the
product blocks that need that user's feature vector. This is achieved by precomputing some
information about the ratings matrix to determine the "out-links" of each user (which blocks of
products it will contribute to) and "in-link" information for each product (which of the feature
vectors it receives from each user block it will depend on). This allows us to send only an
array of feature vectors between each user block and product block, and have the product block
find the users' ratings and update the products based on these messages.

For implicit preference data, the algorithm used is based on
"Collaborative Filtering for Implicit Feedback Datasets", available at
[[http://dx.doi.org/10.1109/ICDM.2008.22]], adapted for the blocked approach used here.

Essentially instead of finding the low-rank approximations to the rating matrix `R`,
this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if
r > 0 and 0 if r <= 0. The ratings then act as 'confidence' values related to strength of
indicated user
preferences rather than explicit ratings given to items.
@classdesc
 
Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
@returns {??}
@class
@memberof module:eclairjs/mllib/recommendation
Alternating Least Squares matrix factorization .