On Sun, Mar 13, 2016 at 05:38:22PM +0800, Zhaoduo WEN wrote: > 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.
Hi Zhaoduo, Can you provide a link to the paper that you are proposing to implement? Also, it would be a good idea to take a look through the existing CF code to see how the sparse linear model you are proposing would fit into the API. Thanks, Ryan -- Ryan Curtin | "Leave the gun. Take the cannoli." [email protected] | - Clemenza _______________________________________________ mlpack mailing list [email protected] https://mailman.cc.gatech.edu/mailman/listinfo/mlpack
