I just looked a little bit am have a few questions. First, these appear to be java implementations for a single machine. How scalable is that? How would it interact with the new math framework?
Second there are a number of style issue like author tags, indentation and such, but what I find most troubling is an almost complete lack of javadoc and complete lack of comments about the origin of the algorithms being used or non-trivial comments about what is happening in the code. I see comments on sections like "update w". That doesn't say anything that the code doesn't say. Sent from my iPhone > On Jan 10, 2015, at 1:45, Andrew Musselman <[email protected]> wrote: > > Non-negative matrix factorization would be a good addition; if you can > include tests with your pull request it will help. > > Assuming this is your PR: https://github.com/apache/mahout/pull/70 > > Looking forward to more. > >> On Jan 9, 2015, at 11:21 PM, 梁明强 <[email protected]> wrote: >> >> Dear sir, >> >> Here is Liang Mingqiang, an undergraduate student, highly interested in >> Recommender System and Mahout. I have implete Non-negative Matrix >> Factorization(NMF) and Probabilistic Matrix Factorization(PMF) method and >> pull request my code for further comment. >> >> I test my code on my computer using movielens dataset and get reasonable >> result. Do I need to write and submit a test module for my code. Just >> because I need dataset for my test, can I add some text files in the test >> package? >> >> In addition, Binary Matrix Factorization seems(BMF) very interesting, I want >> contribute my BMF code for Mahout in the next step. >> >> Last, but not least, Minhash and SimHash are very popular and useful methods >> in Recommender System. But I look through the source code of Mahout, there >> seems no Minhash and SimHash method. Does it mean those methods haven't been >> contributed or just because I haven't check the source code carefully. If >> those two methods have benn contributed, is there anyone willing to tell me >> the path. Thank you! >> >> >> Looking forward, >> ---- >> Liang Mingqiang
