How do you report search results to your users? Users are notorious spellers, and they'll enter text in the wrong case, use the wrong letters, and join words up where they shouldn't.
On our web site, we wanted to report on accurate matches when a user searches, but also recommend other possible search terms when the search finds nothing or very little to report.
A COMPARISON OF PHP "LOOK ALIKE" FUNCTIONS
PHP supports a number of functions that allow the comparison of text strings to see "how similar" they are.
1. soundex returns a 4 character string that represents what a word sounds like when pronounced
2. metaphone creates a variable length character string that also represents what a word sounds like when it's pronounced. More accurate than soundex.
3. similar_text calculates the similarity (as a percentage) between two strings.
4. levenshtein returns the number of changes (character deletes, inserts and replaces) that are needed to transform one string into another. More efficient than similar_text.
You can also use low level string functions and regular expressions to compare text strings - although the four functions listed above are probably more suited for looking for "words like".
APPLICATION OF LOOK ALIKE FUNCTIONS TO A WEB SEARCH
There are nearly 40000 different words used on our web site, and our search pages examine over 5000 different pages. Clearly it's not practical to go through every page and do searches and "look like" comparisons at every search, so here's what we do:
a) When our website is updated, we generate a table of all the words used on the site and their metaphone values. A third column in this table records the number of times each word occurs.
b) When a search fails, we look for all words that we've used with an identical metaphone, and we offer these starting with the most used word first.
c) If the user is searching for a single word and there's still very little we can offer, we attempt to make up a matching metaphone using two words. For example, the metaphone for "dresscode" is TRSKT, so we look to make up that string with words like "dress" and "trees".
d) Stage (c) can generate a very large number of options so we use levenshtein to select those which are nearest to the use's original search term, and to eliminate those which are spelt wildly differently
See also
PHP training courses
Please note that articles in this section of our
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