Vi  t1.txt

1,101,5.0
1,102,3.0
1,103,2.5
2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0
3,101,2.5
3,104,4.0
3,105,4.5
3,107,5.0
4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0
5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5

1.装载数据
scala> import org.apache.spark.mllib.recommendation.{ALS, Rating}
scala> val data = sc.textFile("hdfs://h201:9000/t1.txt")

2.解析原始数据
scala> val ratings = data.map(_.split(",") match { case Array(user, product, rate) =>
  Rating(user.toInt, product.toInt, rate.toDouble)}).cache()

3.查看用户和物品
val users = ratings.map(_.user).distinct()
val products = ratings.map(_.product).distinct()

4.训练数据
rank是模型中隐性因子的个数
scala> val rank = 3
scala> val lambda = 0.01
scala> val numIterations = 2
scala> val model = ALS.train(ratings, rank, numIterations, lambda)

用户评估
scala> val a1=model.userFeatures

商品评估
scala> val a2=model.productFeatures

5.评测
val usersProducts= ratings.map { case Rating(user, product, rate) => (user, product)}

预测
var predictions = model.predict(usersProducts).map { case Rating(user, product, rate) =>((user, product), rate)}

结果与 预测结果合并
val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>((user, product), rate)}.join(predictions)

计算均方误差
val rmse= math.sqrt(ratesAndPreds.map { case ((user, product), (r1, r2)) =>
  val err = (r1 - r2)
  err * err
}.mean())
//  mean()方法，求均值

6.为用户1 ，推荐top3个商品
scala> val userid = 1
scala> val k=3  （推荐个数）
scala> val topKRecs = model.recommendProducts(userid, k)

scala> println(topKRecs.mkString("\n"))

查看用户的历史打分
val goodsForUser=ratings.keyBy(_.user).lookup(1)

7.查看用户下 对某商品的 预测分
val predictedRating = model.predict(1,105)

8.批量推荐
scala> val users = ratings.map(_.user).distinct()
scala> users.collect.flatMap { user =>
  model.recommendProducts(user, 3)}

Vi  t1.txt

 

1,101,5.0

1,102,3.0

1,103,2.5

2,101,2.0

2,102,2.5

2,103,5.0

2,104,2.0

3,101,2.5

3,104,4.0

3,105,4.5

3,107,5.0

4,101,5.0

4,103,3.0

4,104,4.5

4,106,4.0

5,101,4.0

5,102,3.0

5,103,2.0

5,104,4.0

5,105,3.5

 

1.装载数据

scala> import org.apache.spark.mllib.recommendation.{ALS, Rating}

scala> val data = sc.textFile("hdfs://h201:9000/t1.txt")

 

2.解析原始数据

scala> val ratings = data.map(_.split(",") match { case Array(user, product, rate) =>

  Rating(user.toInt, product.toInt, rate.toDouble)}).cache()

 

3. 查看用户和物品

val users = ratings.map(_.user).distinct()

val products = ratings.map(_.product).distinct()

 

4. 训练数据

rank是模型中隐性因子的个数

scala> val rank = 3

scala> val lambda = 0.01

scala> val numIterations = 2

scala> val model = ALS.train(ratings, rank, numIterations, lambda)

 

 

 

用户评估

scala> val a1=model.userFeatures

 

商品评估

scala> val a2=model.productFeatures

 

 

5. 评测

val usersProducts= ratings.map { case Rating(user, product, rate) => (user, product)}

 

预测

var predictions = model.predict(usersProducts).map { case Rating(user, product, rate) =>((user, product), rate)}

 

 

结果与 预测结果合并

val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>((user, product), rate)}.join(predictions)

 

计算均方误差

val rmse= math.sqrt(ratesAndPreds.map { case ((user, product), (r1, r2)) =>

  val err = (r1 - r2)

  err * err

}.mean())

 

  mean()方法，求均值

 

6.为用户1 ，推荐top3个商品

scala> val userid = 1

scala> val k=3  （推荐个数）

scala> val topKRecs = model.recommendProducts(userid, k)

 

scala> println(topKRecs.mkString("\n"))

 

查看用户的历史打分

val goodsForUser=ratings.keyBy(_.user).lookup(1)

 

7.查看用户下 对某商品的 预测分

val predictedRating = model.predict(1,105)

 

8.批量推荐

scala> val users = ratings.map(_.user).distinct()

scala> users.collect.flatMap { user =>

  model.recommendProducts(user, 3)}