Free shipping on all orders over $79* Limited Time Only
Track My Order
Help Centre
Likes
My Wish ListsLogin / Join
Booktopia
Australia's local bookstore
Get Free Shipping on orders over $79
Realtime Data Mining : Self-Learning Techniques for Recommendation Engines - Alexander Paprotny

Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

By: Alexander Paprotny, Michael Thess

eText | 3 December 2013

At a Glance

eText

$159.01

or 4 interest-free payments of $39.75 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's "classic" data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed.

This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.

on
Desktop
Tablet
Mobile

You Can Find This eBook In

Non-FictionScienceScience in GeneralMaths for ScientistsComputing & I.T.Business ApplicationsMathematical & Statistical SoftwareMathematicsApplied MathematicsMathematical ModellingCalculus & Mathematical AnalysisNumerical Analysis
Computer ScienceMathematical Theory of Computation

Other Editions and Formats

Paperback

Published: 27th August 2016

View Product

More in Computer Science

Amazon.com : Get Big Fast - Robert Spector

eBOOK

Amazon.com

Get Big Fast

eBook

$22.99

This is For Everyone - Tim Berners-Lee

eBOOK

This is For Everyone

eBook

$17.99

Gene Expression Analysis : Methods and Protocols - Nalini Raghavachari

eBOOK

Gene Expression Analysis

Methods and Protocols

eBook

RRP $369.00

$332.99

10%
OFF

This product is categorised by