Algorithm to find articles with similar text

The tutorial at this link sounds like it may be what you need. It is easy to follow and works very well.

His algorithm rewards both common substrings and a common ordering of those substrings and so should pick out similar titles quite nicely.

Edit distance isn't a likely candidate, as it would be spelling/word-order dependent, and much more computationally expensive than Will is leading you to believe, considering the size and number of the documents you'd actually be interested in searching.

Something like Lucene is the way to go. You index all your documents, and then when you want to find documents similar to a given document, you turn your given document into a query, and search the index. Internally Lucene will be using tf-idf and an inverted index to make the whole process take an amount of time proportional to the number of documents that could possibly match, not the total number of documents in the collection.

It depends upon your definition of similiar.

The edit-distance algorithm is the standard algorithm for (latin language) dictionary suggestions, and can work on whole texts. Two texts are similiar if they have basically the same words (eh letters) in the same order. So the following two book reviews would be fairly similiar:

1) "This is a great book"

2) "These are not great books"

(The number of letters to remove, insert, delete or alter to turn (2) into (1) is termed the 'edit distance'.)

To implement this you would want to visit every review programmatically. This is perhaps not as costly as it sounds, and if it is too costly you could do the comparisions as a background task and store the n-most-similiar in a database field itself.

Another approach is to understand something of the structure of (latin) languages. If you strip short (non-capitialised or quoted) words, and assign weights to words (or prefixes) that are common or unique, you can do a Bayesianesque comparision. The two following book reviews might be simiplied and found to be similiar:

3) "The french revolution was blah blah War and Peace blah blah France." -> France/French(2) Revolution(1) War(1) Peace(1) (note that a dictionary has been used to combine France and French)

4) "This book is blah blah a revolution in french cuisine." -> France(1) Revolution(1)

To implement this you would want to identify the 'keywords' in a review when it was created/updated, and to find similiar reviews use these keywords in the where-clause of a query (ideally 'full text' searching if the database supports it), with perhaps a post-processing of the results-set for scoring the candidates found.

Books also have categories - are thrillers set in France similiar to historical studies of France, and so on? Meta-data beyond title and text might be useful for keeping results relevant.