/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
var Utils = require('../../utils.js');
var gKernelP;
/**
* 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
*/
/**
* A more class to represent a rating than array[Int, Int, float].
* @classdesc
*/
/**
* @param {integer} user
* @param {integer} product
* @param {float} rating
* @class
* @memberof module:eclairjs/mllib/recommendation
*/
function Rating() {
Utils.handleConstructor(this, arguments, gKernelP);
}
/**
*
* @returns {Promise.<object>}
*/
Rating.prototype.product = function () {
var args = {
target: this,
method: 'product',
//args: Utils.wrapArguments(arguments),
returnType: Number
};
return Utils.generate(args);
};
/**
*
* @returns {Promise.<object>}
*/
Rating.prototype.user = function () {
var args = {
target: this,
method: 'user',
//args: Utils.wrapArguments(arguments),
returnType: Number
};
return Utils.generate(args);
};
/**
*
* @returns {Promise.<object>}
*/
Rating.prototype.rating = function () {
var args = {
target: this,
method: 'rating',
//args: Utils.wrapArguments(arguments),
returnType: Number
};
return Utils.generate(args);
};
Rating.moduleLocation = '/mllib/recommendation/Rating';
module.exports = function(kP) {
if (kP) gKernelP = kP;
return Rating;
};