examples/experimental/scala-parallel-recommendation-entitymap/src/main/scala/ALSAlgorithm.scala

package org.template.recommendation

import io.prediction.controller.PAlgorithm
import io.prediction.controller.Params

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.{Rating => MLlibRating}
import org.apache.spark.mllib.recommendation.ALSModel

import grizzled.slf4j.Logger

case class ALSAlgorithmParams(
  rank: Int,
  numIterations: Int,
  lambda: Double) extends Params

class ALSAlgorithm(val ap: ALSAlgorithmParams)
  extends PAlgorithm[PreparedData, ALSModel, Query, PredictedResult] {

  @transient lazy val logger = Logger[this.type]

  def train(data: PreparedData): ALSModel = {
    // Convert user and item String IDs to Int index for MLlib
    val mllibRatings = data.ratings.map( r =>
      // MLlibRating requires integer index for user and item
      MLlibRating(data.users(r.user).toInt,
        data.items(r.item).toInt, r.rating)
    )
    val m = ALS.train(mllibRatings, ap.rank, ap.numIterations, ap.lambda)
    new ALSModel(
      rank = m.rank,
      userFeatures = m.userFeatures,
      productFeatures = m.productFeatures,
      users = data.users,
      items = data.items)
  }

  def predict(model: ALSModel, query: Query): PredictedResult = {
    // Convert String ID to Int index for Mllib
    model.users.get(query.user).map { userInt =>
      // recommendProducts() returns Array[MLlibRating], which uses item Int
      // index. Convert it to String ID for returning PredictedResult
      val itemScores = model.recommendProducts(userInt.toInt, query.num)
        .map (r => ItemScore(model.items(r.product.toLong), r.rating))
      new PredictedResult(itemScores)
    }.getOrElse{
      logger.info(s"No prediction for unknown user ${query.user}.")
      new PredictedResult(Array.empty)
    }
  }

}