<div dir="ltr">Hi, Ryan,<div><br></div><div>I just knew about GSOC today and I hope it is not late to introduce myself.</div><div><br></div><div>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. </div><div><br></div><div>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. </div><div><br></div><div>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. </div><div><br></div><div>What is your opinion about my proposal? Hope to receive your reply. Thanks.</div><div><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr"><div><div dir="ltr">Best Regards,</div><div dir="ltr"><br><div>Zhaoduo</div></div></div></div></div>
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