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)}
原文地址：https://www.cnblogs.com/xiguage119/p/10722954.html