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From Khurrum Nasim <khurrum.na...@useitc.com>
Subject Re: [Hello] from NASa
Date Sun, 22 May 2016 19:17:07 GMT
Interesting.


> On May 21, 2016, at 10:30 AM, Steven NASa <cj.nasa@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 <pat@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 <smarthi@apache.org> wrote:
>> 
>> Welcome to the project Steven!!
>> 
>> On Fri, May 20, 2016 at 10:07 AM, Steven NASa <cj.nasa@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
>>> ​
>>> 
>> 
>> 


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