[mlpack] GSOC 2016 "Alternatives to neighbour-based collaborative filtering"

[Zhaoduo WEN]

Hi, Ryan,

I just knew about GSOC today and I hope it is not late to introduce myself.

I am Zhaoduo Wen, a senior student from Beijing University of Posts and
Telecommunications.I've been working on data mining and machine learning
problems for a year and had some experience on recommender systems.

>From what I know, although collaborative filtering is faster in prediction
than matrix factorization framework, either itemKNN collaborative filtering
or userKNN collaborative filtering has some drawbacks. One major
disadvantage is that they suffer from low accuracy since there is
essentially no knowledge learned about item characteristics so as to
produce accurate recommendations. However, linear sparse model performs
better both in prediction accuracy and running time. I have read related
papers and I was lucky to listen to the author's presentation.
Consequently, I prefer to using a sparse linear model as the alternatives
to neighborhood-based collaborative filtering.

I am enthusiastic for contributing to this project as I will be extremely
excited if I finish this project and someone uses it in future. I once used
a library for large linear classification (LIBLINEAR), which has a high
citation times on google scholar. I was impressed by its fast and accurate
performance. I wish I could write one someday. I believe GSOC would be a
good beginning.

What is your opinion about my proposal? Hope to receive your reply. Thanks.

-- 
Best Regards,

Zhaoduo
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