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import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.IntObjectProcedure;
import org.apache.mahout.math.map.OpenIntObjectHashMap;

import java.util.Random;

public class Benchmark {

  static int NUM_FEATURES = 20;

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import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.IntObjectProcedure;
import org.apache.mahout.math.map.OpenIntObjectHashMap;

import java.util.Random;

public class Benchmark {

  static int NUM_FEATURES = 20;

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\\

\text{transition matrix}: A \\
\text{rank vector}: x \\
\\

\text{PageRank}: x^{t+1} = A x^t \\

\\
\forall t > 2: \\

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Index: core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/ALSWRFactorizer.java
===================================================================
--- core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/ALSWRFactorizer.java	(Revision 1469532)
+++ core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/ALSWRFactorizer.java	(Arbeitskopie)
@@ -173,6 +173,7 @@
     }
 
     for (int iteration = 0; iteration < numIterations; iteration++) {
+      long start = System.currentTimeMillis();
       log.info("iteration {}", iteration);

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package eu.stratosphere.scala.examples.wordcount

import eu.stratosphere.scala.{ScalaPlan, TextFile}
import eu.stratosphere.pact.common.plan.PlanAssembler


import eu.stratosphere.scala._
import eu.stratosphere.scala.operators._

case class Author(id: Int, name: String)

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package org.apache.spark.examples

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.RDD
import java.util.Random
import scala.collection.mutable
import org.apache.spark.serializer.KryoRegistrator
import com.esotericsoftware.kryo.Kryo

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Keybase proof

I hereby claim:

• I am sscdotopen on github.
• I am sscdotopen (https://keybase.io/sscdotopen) on keybase.
• I have a public key ASBY5f_Vi_i94AxL83aHAH3zGaR_Uq3Ww4yNFXPKDnWeYwo

To claim this, I am signing this object:

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Recommender Example

Input table

Table capturing ratings that users gave to items, with schema: user_id, item_id, rating, date

Preprocessing

• group data by user_id
• remove groups with less than 10 items

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import org.apache.spark.sql.{Row, SaveMode}
import org.apache.spark.sql.types._

val attributesType = new MapType(StringType, StringType, valueContainsNull = false)

val historyEntryType = new StructType()
  .add("intervalStart", LongType)
  .add("intervalEnd", LongType)
  .add("type", StringType)
  .add("attributes", attributesType)

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// 'most_recent_neighbors' is a set of ~500 similar sessions
for neighbor_session in most_recent_neighbors.into_iter() {
  let mut similarity = 0_f64;
  // This returns a HashSet of the items contained in the session
  let other_session_items = index.items_for_session(&neighbor_session.id);
  // The 'evolving_session' is an array of items of length 'num_items_in_evolving_session'   
  for (pos, item_id) in evolving_session.iter().enumerate() {
    if other_session_items.contains(&item_id) {
      let weight = (pos + 1) as f64 / num_items_in_evolving_session as f64;
      similarity += decay_factor;