通过 R 中的余弦相似度为每行检索矩阵中的前 k 个相似行

Question

如何使用R通过余弦相似度有效地检索前K个相似向量？ 询问如何计算一个矩阵的每个向量相对于另一个矩阵的顶部相似向量。 它的回答令人满意，我想调整它以在单个矩阵上运行。

也就是说，我想相关的前k用于在矩阵的每一行类似的其它行。 我怀疑解决方案非常相似，但可以优化。

Answer 1

此功能基于链接的答案：

CosineSimilarities <- function(m, top.k) {
  # Computes cosine similarity between each row and all other rows in a matrix.
  #
  # Args:
  #   m: Matrix of values.
  #   top.k: Number of top rows to show for each row.
  #
  # Returns:
  #   Data frame with columns for pair of rows, and cosine similarity, for top
  #   `top.k` rows per row.
  #   
  # Similarity computation
  cp <- tcrossprod(m)
  mm <- rowSums(m ^ 2)
  result <- cp / sqrt(outer(mm, mm))
  # Top similar rows from train (per row)
  # Use `top.k + 1` to remove the self-reference (similarity = 1)
  top <- apply(result, 2, order, decreasing=TRUE)[seq(top.k + 1), ]
  result.df <- data.frame(row.id1=c(col(top)), row.id2=c(top))
  result.df$cosine.similarity <- result[as.matrix(result.df[, 2:1])]
  # Remove same-row records and return
  return(result.df[result.df$row.id1 != result.df$row.id2, ])
}

例如：

(m <- matrix(1:9, nrow=3))
#      [,1] [,2] [,3]
# [1,]    1    4    7
# [2,]    2    5    8
# [3,]    3    6    9
CosineSimilarities(m, 1)
#   row.id1 row.id2 cosine.similarity
# 2       1       2            0.9956
# 4       2       3            0.9977
# 6       3       2            0.9977