I'm looking for a fuzzy search JavaScript library to filter an array. I've tried using fuzzyset.js and fuse.js, but the results are terrible (there are demos you can try on the linked pages).

After doing some reading on Levenshtein distance, it strikes me as a poor approximation of what users are looking for when they type. For those who don't know, the system calculates how many insertions, deletions, and substitutions are needed to make two strings match.

One obvious flaw, which is fixed in the Levenshtein-Demerau model, is that both blub and boob are considered equally similar to bulb (each requiring two substitutions). It is clear, however, that bulb is more similar to blub than boob is, and the model I just mentioned recognizes that by allowing for transpositions.

I want to use this in the context of text completion, so if I have an array ['international', 'splint', 'tinder'], and my query is int, I think international ought to rank more highly than splint, even though the former has a score (higher=worse) of 10 versus the latter's 3.

So what I'm looking for (and will create if it doesn't exist), is a library that does the following:

• Weights the different text manipulations


• Weights each manipulation differently depending on where they appear in a word (early manipulations being more costly than late manipulations)


• Returns a list of results sorted by relevance

Has anyone come across anything like this? I realize that StackOverflow isn't the place to be asking for software recommendations, but implicit (not anymore!) in the above is: am I thinking about this the right way?

Edit

I found a good paper (pdf) on the subject. Some notes and excerpts:

Affine edit-distance functions assign a relatively lower cost to a sequence of insertions or deletions

the Monger-Elkan distance function (Monge & Elkan 1996), which is an affine variant of the Smith-Waterman distance function (Durban et al. 1998) with particular cost parameters

For the Smith-Waterman distance (wikipedia), "Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure." It's the n-gram approach.

A broadly similar metric, which is not based on an edit-distance model, is the
Jaro metric (Jaro 1995; 1989; Winkler
1999). In the record-linkage literature, good results have been obtained using variants of this method, which is based on the number and order of the common characters between two strings.

A variant of this due to Winkler (1999) also uses the length P of the longest common prefix

(seem to be intended primarily for short strings)

For text completion purposes, the Monger-Elkan and Jaro-Winkler approaches seem to make the most sense. Winkler's addition to the Jaro metric effectively weights the beginnings of words more heavily. And the affine aspect of Monger-Elkan means that the necessity to complete a word (which is simply a sequence of additions) won't disfavor it too heavily.

Conclusion:

the TFIDF
ranking performed best among several token-based distance
metrics, and a tuned affine-gap edit-distance metric proposed by Monge and Elkan performed best among several
string edit-distance metrics. A surprisingly good distance
metric is a fast heuristic scheme, proposed by Jaro and later extended by Winkler.
This works almost as well as the Monge-Elkan scheme, but
is an order of magnitude faster.
One simple way of combining the TFIDF method and the
Jaro-Winkler is to replace the exact token matches used in
TFIDF with approximate token matches based on the Jaro-
Winkler scheme. This combination performs slightly better than either Jaro-Winkler or TFIDF on average, and occasionally performs much better. It is also close in performance to a learned combination of several of the best metrics
considered in this paper.