[mlpack] Alternatives to neighborhood-based collaborative filtering for GSOC 2016

[b venk]

Respected Sir ,
I am Venkatesh , a Computer Science student from India. I am interested in
contributing to the project "Alternatives to neighborhood-based
collaborative filtering".
I have been reading about a few matrix factorization models for the same.
One of the models that I  propose to implement is the  modified SVD
,(SVD++) that makes use of implicit feedback using classes from mlpack::svd
namespace for recommendation.
The method is well described here (here) .
<http://delivery.acm.org/10.1145/1410000/1401944/p426-koren.pdf?ip=182.75.45.1&id=1401944&acc=ACTIVE%20SERVICE&key=045416EF4DDA69D9.CC8C54AC3A0AC65D.4D4702B0C3E38B35.4D4702B0C3E38B35&CFID=760521007&CFTOKEN=40057613&__acm__=1457613157_e2e7b93b4e1710e3a5289515271b1136>

Also I propose to implement models that use time factors , (like the rating
drop of a movie  over time etc). Other models that would be implemented
includes the weight based  kNN (that is mentioned in the project
description). The data that I would be using to test my models would be
from Netflix
and Kaggle. I have been working with C++ for the past 5 years and am good
with  implementation. I request for your valuable suggestions / additional
methods.

I also thank you for contributing to this wonderful project - mlpack.

Thank you.
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