bump!!
On Sep 5, 8:42 pm, Arun yourarunb...@gmail.com wrote:
Hi friends,
Sorry that I am so late to reply.
Thanks much responses have come regarding finding the similarity
between words.
Now what about the searching algorithm from the 'millions of words'. I
think they expected an answer
Does similarity here refer to similarity in strings or similar to items in
same category ?
If its similarity to strings then edit distance can be used here. But if its
the latter, then how will edit distance help ?
It would probably be only looking for items in the same category.
On Thu, Sep 2,
I think Gene is right. they are asking more than search here. Thanks
to Gene for suggesting this idea :)
now the how part,
we can have a level of abstraction:
no of products matching to name --- use trie, suffix tree, some
genetic algo, string matching, ms tree..
based on products
Trie-traverse search will be best..
On Thu, Sep 2, 2010 at 8:10 PM, Manjunath Manohar
manjunath.n...@gmail.comwrote:
trie will be the best choice for this..
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trie will be the best choice for this..
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For
HI Arun,
This is the edit distance problem which can be solved using DP.
Calculate the cost for each product on the fly and return the top
products with the least edit cost!
On Sep 1, 5:15 pm, Arun yourarunb...@gmail.com wrote:
You are given the amazon.com database which consists of names of
Edit distance is one way to determine similarity. It assumes all
differences are typographic. Amazon is probably interested in many
other forms of similarity. When someone types audio system in the
Amazon search, you want them to see receivers, speakers, tuners, etc.
It's very possible this
Even if you're only matching words, there are different kinds of
similarity. Check out the soundex algorithm, for example. Levenshtein
distance. The Hungarian algorithm. What does 50% similarity mean
anyway? I know of no accepted meaning.
My point is that if you're in an interview situation
