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 > > > > > >
