Using Spark and Shark to Power a Real-time Recommendation.....- N. Pentreath (Graphflow)
Using Spark and Shark to Power a Real-time Recommendation and Customer Intelligence Platform Nick Pentreath (Graphflow)
Scalable Distributed Decision Trees in Spark MLLib - M. Amde, H. Das, E. Sparks & A. Talwalkar
Scalable Distributed Decision Trees in Spark MLLib Manish Amde (Origami Logic), Hirakendu Das (Yahoo! Inc.), Evan Sparks (UC Berkeley), Ameet Talwalkar ...
|Advanced Analytics with Spark - O'Reilly Media|
This practical guide shows you how to harness Spark?s power for approaching a variety of analytics problems. You?ll learn how to apply common techniques, such as classification, clustering, collaborative filtering, anomaly detection,...
|New Features in MLlib in Spark 1.0 ? Databricks|
Blog : MLlib is a Spark component focusing on machine learning. It became a standard component of Spark in version 0.8 (Sep 2013). The initial contribution was from Berkeley AMPLab. Since then, 50+ developers from the open source community have contributed to its codebase. With the release of Spark 1.0, I?m glad to share some of the new features in MLlib. Among the most important ones are:
|Distributing the Singular Value Decomposition with Spark ? Databricks|
Blog : The Singular Value Decomposition (SVD) is one of the cornerstones of linear algebra and has widespread application in many real-world modeling situations. Problems such as recommender systems, linear systems, least squares, and many others can be solved using the SVD. It is frequently used in statistics where it is related to principal component analysis (PCA) and to correspondence analysis, and in signal processing and pattern recognition. Another usage is latent semantic indexing in natural language processing.
|Scalable Collaborative Filtering with Spark MLlib ? Databricks|
Blog : Recommendation systems are among the most popular applications of machine learning. The idea is to predict whether a customer would like a certain item: a product, a movie, or a song. Scale is a key concern for recommendation systems, since computational complexity increases with the size of a company?s customer base. In this blog post, we discuss how Spark MLlib enables building recommendation models from billions of records in just a few lines of Python (Scala/Java APIs also available).
Sparse data support in MLlib - Xiangrui Meng (Databricks)
Sparse data support in MLlib Xiangrui Meng (Databricks)