用户电影评分数据集下载

http://grouplens.org/datasets/movielens/

1) Item-Based,非个性化的，每个人看到的都一样

2) User-Based，个性化的，每个人看到的不一样

对用户的行为分析得到用户的喜好后，可以根据用户的喜好计算相似用户和物品，然后可以基于相似用户或物品进行推荐。这就是协同过滤中的两个分支了，基于用户的和基于物品的协同过滤。

在计算用户之间的相似度时，是将一个用户对所有物品的偏好作为一个向量，而在计算物品之间的相似度时，是将所有用户对某个物品的偏好作为一个向量。求出相似度后，接下来可以求相似邻居了。

3）基于模型（ModelCF）

按照模型，可以分为：

1）最近邻模型：基于距离的协同过滤算法

       2）Latent Factor Mode（SVD）：基于矩阵分解的模型

       3）Graph：图模型，社会网络图模型

适用场景

对于一个在线网站，用户的数量往往超过物品的数量，同时物品数据相对稳定，因此计算物品的相似度不但

计算量小，同时不必频繁更新。但是这种情况只适用于电子商务类型的网站，像新闻类，博客等这类网站的

系统推荐，情况往往是相反的，物品数量是海量的，而且频繁更新。

r语言实现基于物品的协同过滤算法

  #引用plyr包library(plyr)#读取数据集train<-read.table(file="C:/users/Administrator/Desktop/u.data",sep=" ")train<-train[1:3]names(train)<-c("user","item","pref")#计算用户列表方法usersUnique<-function(){users<-unique(train$user)users[order(users)]}#计算商品列表方法itemsUnique<-function(){items<-unique(train$item)items[order(items)]}# 用户列表users<-usersUnique()# 商品列表items<-itemsUnique() #建立商品列表索引index<-function(x) which(items %in% x)data<-ddply(train,.(user,item,pref),summarize,idx=index(item)) #同现矩阵cooccurrence<-function(data){n<-length(items)co<-matrix(rep(0,n*n),nrow=n)for(u in users){idx<-index(data$item[which(data$user==u)])m<-merge(idx,idx)for(i in 1:nrow(m)){co[m$x[i],m$y[i]]=co[m$x[i],m$y[i]]+1}}return(co)}#推荐算法recommend<-function(udata=udata,co=coMatrix,num=0){n<-length(items)# all of prefpref<-rep(0,n)pref[udata$idx]<-udata$pref# 用户评分矩阵userx<-matrix(pref,nrow=n)# 同现矩阵*评分矩阵r<-co %*% userx# 推荐结果排序 # 把该用户评分过的商品的推荐值设为0r[udata$idx]<-0idx<-order(r,decreasing=TRUE)topn<-data.frame(user=rep(udata$user[1],length(idx)),item=items[idx],val=r[idx])topn<-topn[which(topn$val>0),]# 推荐结果取前num个if(num>0){topn<-head(topn,num)}#返回结果return(topn)}#生成同现矩阵co<-cooccurrence(data) #计算推荐结果recommendation<-data.frame()for(i in 1:length(users)){udata<-data[which(data$user==users[i]),]recommendation<-rbind(recommendation,recommend(udata,co,0)) }

mareduce 实现

参考文章：

http://www.cnblogs.com/anny-1980/articles/3519555.html

代码下载

https://github.com/bsspirit/maven_hadoop_template/releases/tag/recommend

spark ALS实现

Spark mllib里用的是矩阵分解的协同过滤，不是UserBase也不是ItemBase。

参考文章：

http://www.mamicode.com/info-detail-865258.html

import org.apache.spark.SparkConf
import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
import org.apache.spark.rdd._
import org.apache.spark.SparkContext
import scala.io.Sourceobject MovieLensALS {def main(args:Array[String]) {//设置运行环境val sparkConf = new SparkConf().setAppName("MovieLensALS").setMaster("local[5]")val sc = new SparkContext(sparkConf)//装载用户评分，该评分由评分器生成(即生成文件personalRatings.txt)val myRatings = loadRatings(args(1))val myRatingsRDD = sc.parallelize(myRatings, 1)//样本数据目录val movielensHomeDir = args(0)//装载样本评分数据，其中最后一列Timestamp取除10的余数作为key，Rating为值，即(Int，Rating)val ratings = sc.textFile(movielensHomeDir + "/ratings.dat").map {line =>val fields = line.split("::")// format: (timestamp % 10, Rating(userId, movieId, rating))(fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))}//装载电影目录对照表(电影ID->电影标题)val movies = sc.textFile(movielensHomeDir + "/movies.dat").map {line =>val fields = line.split("::")// format: (movieId, movieName)(fields(0).toInt, fields(1))}.collect().toMap//统计有用户数量和电影数量以及用户对电影的评分数目val numRatings = ratings.count()val numUsers = ratings.map(_._2.user).distinct().count()val numMovies = ratings.map(_._2.product).distinct().count()println("Got " + numRatings + " ratings from " + numUsers + " users " + numMovies + " movies")//将样本评分表以key值切分成3个部分，分别用于训练 (60%，并加入用户评分), 校验 (20%), and 测试 (20%)//该数据在计算过程中要多次应用到，所以cache到内存val numPartitions = 4val training = ratings.filter(x => x._1 < 6).values.union(myRatingsRDD).repartition(numPartitions).persist()val validation = ratings.filter(x => x._1 >= 6 && x._1 < 8).values.repartition(numPartitions).persist()val test = ratings.filter(x => x._1 >= 8).values.persist()val numTraining = training.count()val numValidation = validation.count()val numTest = test.count()println("Training: " + numTraining + " validation: " + numValidation + " test: " + numTest)//训练不同参数下的模型，并在校验集中验证，获取最佳参数下的模型val ranks = List(8, 12)val lambdas = List(0.1, 10.0)val numIters = List(10, 20)var bestModel: Option[MatrixFactorizationModel] = Nonevar bestValidationRmse = Double.MaxValuevar bestRank = 0var bestLambda = -1.0var bestNumIter = -1for (rank <- ranks; lambda <- lambdas; numIter <- numIters) {val model = ALS.train(training, rank, numIter, lambda)val validationRmse = computeRmse(model, validation, numValidation)println("RMSE(validation) = " + validationRmse + " for the model trained with rank = "+ rank + ",lambda = " + lambda + ",and numIter = " + numIter + ".")if (validationRmse < bestValidationRmse) {bestModel = Some(model)bestValidationRmse = validationRmsebestRank = rankbestLambda = lambdabestNumIter = numIter}}//用最佳模型预测测试集的评分，并计算和实际评分之间的均方根误差（RMSE）val testRmse = computeRmse(bestModel.get, test, numTest)println("The best model was trained with rank = " + bestRank + " and lambda = " + bestLambda+ ", and numIter = " + bestNumIter + ", and its RMSE on the test set is " + testRmse + ".")//create a naive baseline and compare it with the best modelval meanRating = training.union(validation).map(_.rating).mean()val baselineRmse = math.sqrt(test.map(x => (meanRating - x.rating) * (meanRating - x.rating)).reduce(_ + _) / numTest)val improvement = (baselineRmse - testRmse) / baselineRmse * 100println("The best model improves the baseline by " + "%1.2f".format(improvement) + "%.")//推荐前十部最感兴趣的电影，注意要剔除用户已经评分的电影val myRatedMovieIds = myRatings.map(_.product).toSetval candidates = sc.parallelize(movies.keys.filter(!myRatedMovieIds.contains(_)).toSeq)val recommendations = bestModel.get.predict(candidates.map((0, _))).collect().sortBy(-_.rating).take(10)var i = 1println("Movies recommended for you:")recommendations.foreach { r =>println("%2d".format(i) + ": " + movies(r.product))i += 1}sc.stop()}/** 校验集预测数据和实际数据之间的均方根误差 **/def computeRmse(model:MatrixFactorizationModel,data:RDD[Rating],n:Long):Double = {val predictions:RDD[Rating] = model.predict(data.map(x => (x.user,x.product)))val predictionsAndRatings = predictions.map{ x =>((x.user,x.product),x.rating)}.join(data.map(x => ((x.user,x.product),x.rating))).valuesmath.sqrt(predictionsAndRatings.map( x => (x._1 - x._2) * (x._1 - x._2)).reduce(_+_)/n)}/** 装载用户评分文件 personalRatings.txt **/def loadRatings(path:String):Seq[Rating] = {val lines = Source.fromFile(path).getLines()val ratings = lines.map{line =>val fields = line.split("::")Rating(fields(0).toInt,fields(1).toInt,fields(2).toDouble)}.filter(_.rating > 0.0)if(ratings.isEmpty){sys.error("No ratings provided.")}else{ratings.toSeq}}}

参考文章：

http://blog.csdn.net/acdreamers/article/details/44672305

http://www.cnblogs.com/technology/p/4467895.html

http://blog.fens.me/rhadoop-mapreduce-rmr/

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