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Spark怎样实现推荐系统中的相似度算法

发表于：2025-12-02 作者：千家信息网编辑

千家信息网最后更新 2025年12月02日，Spark怎样实现推荐系统中的相似度算法，针对这个问题，这篇文章详细介绍了相对应的分析和解答，希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。在推荐系统中，协同过滤算法是应用较多的，具体又

千家信息网最后更新 2025年12月02日Spark怎样实现推荐系统中的相似度算法

Spark怎样实现推荐系统中的相似度算法，针对这个问题，这篇文章详细介绍了相对应的分析和解答，希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。

在推荐系统中，协同过滤算法是应用较多的，具体又主要划分为基于用户和基于物品的协同过滤算法，核心点就是基于"一个人"或"一件物品"，根据这个人或物品所具有的属性，比如对于人就是性别、年龄、工作、收入、喜好等，找出与这个人或物品相似的人或物，当然实际处理中参考的因子会复杂的多。

下面不介绍相关数学概念，主要给出常用的相似度算法代码实现，并且同一算法有多种实现方式。

欧几里得距离

def euclidean2(v1: Vector, v2: Vector): Double = {    require(v1.size == v2.size, s"SimilarityAlgorithms:Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +      s"=${v2.size}.")
    val x = v1.toArray    val y = v2.toArray
    euclidean(x, y)  }
  def euclidean(x: Array[Double], y: Array[Double]): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:Array length do not match: Len(x)=${x.length} and Len(y)" +      s"=${y.length}.")
    math.sqrt(x.zip(y).map(p => p._1 - p._2).map(d => d * d).sum)  }    def euclidean(v1: Vector, v2: Vector): Double = {    val sqdist = Vectors.sqdist(v1, v2)    math.sqrt(sqdist)  }

皮尔逊相关系数

 def pearsonCorrelationSimilarity(arr1: Array[Double], arr2: Array[Double]): Double = {    require(arr1.length == arr2.length, s"SimilarityAlgorithms:Array length do not match: Len(x)=${arr1.length} and Len(y)" +      s"=${arr2.length}.")
    val sum_vec1 = arr1.sum    val sum_vec2 = arr2.sum
    val square_sum_vec1 = arr1.map(x => x * x).sum    val square_sum_vec2 = arr2.map(x => x * x).sum
    val zipVec = arr1.zip(arr2)
    val product = zipVec.map(x => x._1 * x._2).sum    val numerator = product - (sum_vec1 * sum_vec2 / arr1.length)
    val dominator = math.pow((square_sum_vec1 - math.pow(sum_vec1, 2) / arr1.length) * (square_sum_vec2 - math.pow(sum_vec2, 2) / arr2.length), 0.5)    if (dominator == 0) Double.NaN else numerator / (dominator * 1.0)  }

余弦相似度

  /** jblas实现余弦相似度 */  def cosineSimilarity(v1: DoubleMatrix, v2: DoubleMatrix): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:Array length do not match: Len(v1)=${x.length} and Len(v2)" +      s"=${y.length}.")          v1.dot(v2) / (v1.norm2() * v2.norm2())  }  def cosineSimilarity(v1: Vector, v2: Vector): Double = {    require(v1.size == v2.size, s"SimilarityAlgorithms:Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +      s"=${v2.size}.")
    val x = v1.toArray    val y = v2.toArray
    cosineSimilarity(x, y)  }
    def cosineSimilarity(x: Array[Double], y: Array[Double]): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:Array length do not match: Len(x)=${x.length} and Len(y)" +      s"=${y.length}.")
    val member = x.zip(y).map(d => d._1 * d._2).sum       val temp1 = math.sqrt(x.map(math.pow(_, 2)).sum)    val temp2 = math.sqrt(y.map(math.pow(_, 2)).sum)
    val denominator = temp1 * temp2    if (denominator == 0) Double.NaN else member / (denominator * 1.0)  }

修正余弦相似度

   def adjustedCosineSimJblas(x: DoubleMatrix, y: DoubleMatrix): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:DoubleMatrix length do not match: Len(x)=${x.length} and Len(y)" +      s"=${y.length}.")
    val avg = (x.sum() + y.sum()) / (x.length + y.length)    val v1 = x.sub(avg)    val v2 = y.sub(avg)    v1.dot(v2) / (v1.norm2() * v2.norm2())  }
   def adjustedCosineSimJblas(x: Array[Double], y: Array[Double]): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:Array length do not match: Len(x)=${x.length} and Len(y)" +      s"=${y.length}.")
    val v1 = new DoubleMatrix(x)    val v2 = new DoubleMatrix(y)
    adjustedCosineSimJblas(v1, v2)  }    def adjustedCosineSimilarity(v1: Vector, v2: Vector): Double = {    require(v1.size == v2.size, s"SimilarityAlgorithms:Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +      s"=${v2.size}.")    val x = v1.toArray    val y = v2.toArray
    adjustedCosineSimilarity(x, y)  }
  def adjustedCosineSimilarity(x: Array[Double], y: Array[Double]): Double = {    require(x.length == y.length, s"SimilarityAlgorithms:Array length do not match: Len(x)=${x.length} and Len(y)" +      s"=${y.length}.")
    val avg = (x.sum + y.sum) / (x.length + y.length)
    val member = x.map(_ - avg).zip(y.map(_ - avg)).map(d => d._1 * d._2).sum
    val temp1 = math.sqrt(x.map(num => math.pow(num - avg, 2)).sum)    val temp2 = math.sqrt(y.map(num => math.pow(num - avg, 2)).sum)
    val denominator = temp1 * temp2    if (denominator == 0) Double.NaN else member / (denominator * 1.0)  }