Yak shaving a good place to eat using non negative matrix factorization
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| Title | Yak shaving a good place to eat using non negative matrix factorization |
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| Part Number | 90 |
| Number of Parts | 173 |
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| License | You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this license. |
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| Production Place | Bilbao, Euskadi, Spain |
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| Abstract | Adriano Petrich - Yak shaving a good place to eat using non negative matrix factorization
Trying to find a good place to eat has become much easier and
democratic with online reviews, but on the other hand, that creates
new problems. Can you trust that 5 star review of fast food chain as
much as the 1 star of a fancy restaurant because "Toast arrived far
too early, and too thin"?
We all like enjoy things differently. Starting of on the assumption
that the "best pizza" is not the same for everyone. Can we group
users into people that has similar tastes? Can we identify reviews
and restaurants to make sense of it? Can that lead us to a better way
to find restaurants that you like?
Using some data handling techniques I walk you through my process and
results that I've got from that idea. There are no requisites for
this talk except basic python and math knowledge (matrices exist) |
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