Article Discussion: Matching Strings and Algorithms

When you go to a search engine and type in a keyword, how does the engine know what to look for? Even when a word is misspelled, still the results come back, sometimes not how we like, but often pretty close. How do these search engines compare the input to the database items and return with a result? Learn about this key part of driving traffic to your site in this article by Dr. Simon White.


Read the full article here: Matching Strings and Algorithms

This is a great article for people like myself who are starting to learn more and more about SEO/SEM. This type of article can give me the basis to really get precise.

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interesting and educational unlike most artciles here which are tutorials in nature.

does keyword density play a very important role in SEO?

IMO, Google uses a lot of factor to determine on a page which are related like anchor text from other website, Page Content and also linked to authority site and hub site. Google cannot use only a few of algorithms.

It's interesting the article uses Amazon as an example, even the best have room for improvement. We use the Amazon web service on Book Cost, and even now it has trouble with simple things like apostrophes. You can see it by searching for something like "dont make me think" instead of "don't make me think." If you do a title search in the advanced area, the first search won't find the book.

They've since added some kind of post-search processing on the main searches that return no results, and we'll likely do the same, but it's sometimes funny how what seems like a relatively simple thing doesn't work like it should.

This was worth reading. Our company is considering the use of search on a retail site but we have seen bad algos on some of our competitor's sites. What we plan to do instead is to write an inhouse substitute for a search that grabs the important terms from the querry and then serve the results that "WE WANT" the customer to see. This leaves little to chance from a marketing perspective. We already know the search terms used by examining the logs and about 100 terms seems to be the universe. This article gave me some ideas on how we can narrow the list farther. Thanks.