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import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
import scala.util.parsing.json.JSONObject
// Load and parse the data
val data = sc.textFile("blog-recommendation/trainPostsFinal_user_item_cf")
val ratings = data.map(_.split('\t') match { case Array(user, item, rate) =>
Rating(user.toInt, item.toInt, rate.toDouble)
})
// Build the recommendation model using ALS
val rank = 10
val numIterations = 10
val model = ALS.train(ratings, rank, numIterations, 0.01)
// Evaluate the model on rating data
val usersProducts = ratings.map { case Rating(user, product, rate) =>
(user, product)
}
val predictions =
model.predict(usersProducts).map { case Rating(user, product, rate) =>
((user, product), rate)
}
val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
((user, product), rate)
}.join(predictions)
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
println("Mean Squared Error = " + MSE)
def writeModelFeaturesAsTensor (modelFeatures:(Int, Array[Double]), id_string:String) = {
val id = modelFeatures._1
val latentVector = modelFeatures._2
var latentVectorMap:Map[String,Double] = Map()
var output:Map[String,Any] = Map()
for ( i <- 0 until latentVector.length ){
latentVectorMap += (("user_item_cf:" + i.toString, latentVector(i)))
}
output += ((id_string, id))
output += (("user_item_cf", scala.util.parsing.json.JSONObject(latentVectorMap)))
JSONObject(output)
}
val product_features = model.productFeatures.map(x => writeModelFeaturesAsTensor(x, "post_id"))
product_features.saveAsTextFile("blog-recommendation/user_item_cf/product_features")
val user_features = model.userFeatures.map(x => writeModelFeaturesAsTensor(x, "user_id"))
user_features.saveAsTextFile("blog-recommendation/user_item_cf/user_features")
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