FuzzyCompare

Getting started

In order to compare two strings with each other do the following:

iex> FuzzyCompare.similarity("Oscar-Claude Monet", "monet, claude")
0.95

Inner workings

Imagine you had to match some names.

Try to match the following list of painters:

• "Oscar-Claude Monet"
• "Edouard Manet"
• "Monet, Claude"

For a human it is easy to see that some of the names have just been flipped
and that others are different but similar sounding.

A first approrach could be to compare the strings with a string similarity
function like the
Jaro-Winkler
function.

iex> String.jaro_distance("Oscar-Claude Monet", "Monet, Claude")
0.5407763532763533
iex> String.jaro_distance("Oscar-Claude Monet", "Edouard Manet")
0.624928774928775

This is not an improvement over exact equality.

In order to improve the results this library uses two different approaches,
FuzzyCompare.ChunkSet and FuzzyCompare.SortedChunks.

Sorted chunks

This approach yields good results when words within a string have been
shuffled around. The strategy will sort all substrings by words and compare
the sorted strings.

iex> FuzzyCompare.SortedChunks.substring_similarity("Oscar-Claude Monet", "Monet, Claude")
1.0
iex(4)> FuzzyCompare.SortedChunks.substring_similarity("Oscar-Claude Monet", "Edouard Manet")
0.6944444444444443

Chunkset

The chunkset approach is best in scenarios when the strings contain other
substrings that are not relevant to what is being searched for.

iex> FuzzyCompare.ChunkSet.standard_similarity("Claude Monet", "Alice Hoschedé was the wife of Claude Monet")
1.0

Substring comparison

Should one of the strings be much longer than the other the library will
attempt to compare matching substrings only.

Credits

This library is inspired by a seatgeek blogpost from 2011.