Interesting.
> On May 21, 2016, at 10:30 AM, Steven NASa <cj.n...@gmail.com> wrote: > > Hi Pat, > > Thank you for your reply, I fully understand that core algorithms and data > are 2 different part of the system, this is why we have 2 major idea: "Big > data" and "Machine Learning". > > My requirements of Recommenders are just like what Amazon does: Item-based, > but the number of items and users is very big, so there comes to a very > huge matrix. So I am still learning using Mahout to make the matrix > computing on a distributed system. After I am familiar with Mahout, I think > I can have some works on GPU acceleration for Matrix computing and some > other mathematical optimization. > About the data prep, I think we can define an abstraction of > conventions in data > prep, data ingestion, and serving components. Users can following some > conventions to feed data to Mahout. > > Steven NASa > 2016/05/21 > > 2016-05-21 22:06 GMT+08:00 Pat Ferrel <p...@occamsmachete.com>: > >> Hi Stephen, >> >> We have implemented SVD, ALS, and CCO for recommender, but these are only >> core algorithms, not really recommenders as Mahout has done in the past. >> The reason for this is that there are data prep, data ingestion, and >> serving components that, in a modern system, must be supplied also. So far >> Mahout has stayed aways from actually including servers, either for input >> of output. >> >> That said there is plenty of room for algorithm development in Mahout. I >> worked on the CCO algorithm, which uses PredictionIO (proposed for the >> Apache Incubator) to supply the serving components. >> >> Someone with your experience in real-life use of recommenders is certainly >> welcome. >> >> What type of project did you have in mind? >> >> >> On May 20, 2016, at 10:00 AM, Suneel Marthi <smar...@apache.org> wrote: >> >> Welcome to the project Steven!! >> >> On Fri, May 20, 2016 at 10:07 AM, Steven NASa <cj.n...@gmail.com> wrote: >> >>> Hi Folk & Masters, >>> >>> My name is *NASa*. I am now working for an e-commerce B2C company in >> China, >>> dealing with Transaction Process development in C++ & Java on Linux >>> environment. >>> >>> As you know, *Recommender System* is quite valuable and important to an >>> e-commerce online shopping website like Amazon. I was told and required >> to >>> design and implement a Recommender System which can bring some value to >> my >>> Company. Our System is based on C++ codes. So I was searching for an >> robust >>> Machine Learning framework in C++ which can help me to easily implement a >>> Recommender System. I did not find any one which can satisfy my >>> requirements, but only some C++ math libraries. >>> >>> Our system is based on an internal distributed frameworks like RPC and DB >>> access on Linux environment based on C++ programming language. But I find >>> it is really inconvenient to implement a Recommender System in C++ from >>> zero without distributed computing library supporting, like >>> implementing *Collaborative >>> Filtering* with SVD in a distributed computing way. So I am trying to >> find >>> a framework/library with is designed based on Distributed-System. There I >>> come to *Mahout*. >>> >>> I wish I can build a library that can help people easily and quickly >> build >>> up a Recommender System based on Distributed System and also use the >>> Machine Learning Algorithms in distributed way. Apache has many amazing >>> projects which can help people to build up robust distributed system >>> easily. So I am moving to using “Java” environment. >>> >>> I am new to *Mahout* and *Hadoop*, *Spark*, *Scala* and I learned Andrew >>> Ng’s “Machine Learning” from Coursera >>> <https://www.coursera.org/learn/machine-learning/home/welcome>. So I >> have >>> the basic knowledge of Machine Learning, and now I am keeping forward to >>> *Deep >>> Learning* and *Convex Optimization*, some other Mathematical Optimization >>> implementation. I am now still learning and getting famiIiar with >> Mahout. I >>> hope I can contribute some codes to Mahout in the early future with >>> learning by coding and coding by learning. >>> NASa 2016/05/20 >>> >>> >> >>
