<div dir="ltr">Respected Sir ,<br>I am Venkatesh , a Computer Science student from India. I am interested in contributing to the project "Alternatives to neighborhood-based collaborative filtering".<br>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.<br>The method is well described here<a href="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"> (here) .</a><br><br>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 <br>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 <br>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 <br>methods. <br><br>I also thank you for contributing to this wonderful project - mlpack.<br><br>Thank you.<br></div>