不要迷信相关性，用相似性代替相关性,对SEO作用可能更大
本帖最后由 老飘 于 2014-7-24 17:08 编辑
不要迷信相关性，不要太陷入先验概率、后验概率等NLP领域的概念，没有深厚的算法基础和编程能力，这些根本就无法实现。
对于大部分SEO从业者来说，执行、简单、条理清晰可能更为重要。
对SEO来说，相似性的效果可能会更好（页面调取），只简单计算页面title的相似性就行。
有一个简单的算法：编辑距离，可以求字符串相似性，算法如下，可直接使用。
def levenshtein(a,b):
"Calculates the Levenshtein distance between a and b."
n, m = len(a), len(b)
if n > m:
# Make sure n <= m, to use O(min(n,m)) space
a,b = b,a
n,m = m,n
current = range(n+1)
for i in range(1,m+1):
previous, current = current, +[0]*n
for j in range(1,n+1):
add, delete = previous[j]+1, current[j-1]+1
change = previous[j-1]
if a[j-1] != b[i-1]:
change = change + 1
current[j] = min(add, delete, change)
return current[n]
def levenshtein_distance(first, second):
"""Find the Levenshtein distance between two strings."""
if len(first) > len(second):
first, second = second, first
if len(second) == 0:
return len(first)
first_length = len(first) + 1
second_length = len(second) + 1
distance_matrix = [range(second_length) for x in range(first_length)]
for i in range(1, first_length):
for j in range(1, second_length):
deletion = distance_matrix[i-1][j] + 1
insertion = distance_matrix[j-1] + 1
substitution = distance_matrix[i-1][j-1]
if first[i-1] != second[j-1]:
substitution += 1
distance_matrix[j] = min(insertion, deletion, substitution)
return distance_matrix[first_length-1][second_length-1]
算法转载：http://blog.csdn.net/haichao062/article/details/8079748
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