[mlpack] (no subject)
Divyam Khandelwal
khandelwal.divyam at gmail.com
Mon Mar 7 12:36:26 EST 2016
Hello Sir Ryan Curtin and Sumedh Ghaisas,
I read about archives in mailing list so I firstly build mlpack
library and I had compile mlpack tutorial programs and now I am comfortable
with it.Thanks for helping in completing my first step. I am Third Year
B.Tech student CS Undergraduate from Vishwakarma Institute of
Technology,Pune.I’m been an active C,C++ coder for many years now. I’m
currently studying Recommender Systems and I want to explore it lot by
learning new optimization methods and by implementing it.However, while it
is possible for me to improve my knowledge of recommender system on the
side while studying machine learning (most of my recommender system was
self-taught anyway).I’ve always had a passion for both programming and
mathematics.
So I want to move towards the second step to solve bug please direct me
how to proceed toward it
I'm interesrted in Alternatives to neighborhood-based collaborative
filtering
I read about the paper attached on the site. Even I read the matrix
factorization method of Amazon and Netflix which they used and understood
it.
The reference material I read uptill now is
=>The given paper
=>Slideshare giving a brief idea about matrix factorization
https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0ahUKEwjspY2ngK_LAhXOTI4KHU5NC8kQFgg8MAQ&url=http%3A%2F%2Fwww.slideshare.net%2Fstudentalei%2Fmatrix-factorization-techniques-for-recommender-systems&usg=AFQjCNEtwt1uY6UqtIRV595sg9KSkCX2vg&sig2=OWdhVM1sjWXiSRw10VaCGA&bvm=bv.116274245,d.c2E
=>Matrix Factorization Techniques for Recommender Systems
By Yehuda Koren, Yahoo Research
Robert Bell and Chris Volinsky, AT&T Labs—Research
Please recommend what literature should i read and what top of bugs should
i solve in this topic
Thanking You
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mailman.cc.gatech.edu/pipermail/mlpack/attachments/20160307/aaf4d879/attachment.html>
More information about the mlpack
mailing list