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Dernière mise à jour : 11/03/2009 - 1 livres - 1 critiques


couverture du livre 'Collective Intelligence in Action'

Note 4.0

Collective Intelligence in Action

de Satnam Alag
Public visé : avancé

Résumé de l'éditeur

Product Description

There's a great deal of wisdom in a crowd, but how do you listen to a thousand people talking at once? Identifying the wants, needs, and knowledge of internet users can be like listening to a mob.

In the Web 2.0 era, leveraging the collective power of user contributions, interactions, and feedback is the key to market dominance. A new category of powerful programming techniques lets you discover the patterns, inter-relationships, and individual profiles-the collective intelligence--locked in the data people leave behind as they surf websites, post blogs, and interact with other users.

Collective Intelligence in Action is a hands-on guidebook for implementing collective intelligence concepts using Java. It is the first Java-based book to emphasize the underlying algorithms and technical implementation of vital data gathering and mining techniques like analyzing trends, discovering relationships, and making predictions. It provides a pragmatic approach to personalization by combining content-based analysis with collaborative approaches.

This book is for Java developers implementing Collective Intelligence in real, high-use applications. Following a running example in which you harvest and use information from blogs, you learn to develop software that you can embed in your own applications. The code examples are immediately reusable and give the Java developer a working collective intelligence toolkit.

Along the way, you work with, a number of APIs and open-source toolkits including text analysis and search using Lucene, web-crawling using Nutch, and applying machine learning algorithms using WEKA and the Java Data Mining (JDM) standard.

About the Author

Satnam Alag, PhD, is currently the Vice President of Engineering at NextBio, a vertical search engine and a Web 2.0 collaboration application for the life sciences community. He is a seasoned software professional with over fifteen years of experience in machine learning and over a decade of experience in commercial software development and management. Dr. Alag worked as a consultant with Johnson & Johnsons's BabyCenter where he helped develop their personalization engine. Prior to that, he was the Chief Software Architect at Rearden Commerce and began his career at GE R&D. He is a Sun Certified Enterprise Architect (SCEA) for the Java Platform. Dr. Alag earned his PhD in engineering from UC Berkeley and his dissertation was in the area of probabilistic reasoning and machine learning. He has published numerous peer-reviewed articles.

Critique du livre par Chabli Faisel
A mon avis, ce livre est destiné à des gens ayant de bonnes connaissances en Business Intelligence ainsi qu'en Java pour bien comprendre les exemples fournis, mais qui reste un livre pratique pour l'application de l'intelligence collective à des applications web.

Etant divisé en trois grandes parties, le livre nous introduit, à travers le 1er, 2ème et 3ème chapitres, au domaine de l'intelligence collective en nous donnant un aperçu de l'architecture nécessaire pour intégrer l'intelligence collective dans une application.

La 2ème partie du livre traite l'intelligence provenant de la collecte de données. A travers le 7ème chapitre, le lecteur est introduit au processus du data mining ainsi que ses différents types d'algorithmes. L'outil utilisé étant WEKA, un outil open source pour le data mining dont l'utilisation sera plus explicite dans le chapitre 9 à travers des exemples de clustering.

La dernière partie traite l'application de l'intelligence dans une application.

Les points forts du livre résident dans la méthodologie adoptée par l'auteur pour expliquer comment appliquer de l'intelligence collective dans une application web. Ainsi, le livre nous mène, à travers de nombreux exemples, à comprendre comment appliquer de l'intelligence collective, en dénombrant ses bénéfices, dans une application web. Chaque concept est ainsi illustré par des exemples concrets. L'auteur cite plusieurs sites de référence utilisant le concept de l'intelligence collective, chose qui peut donner plus de visibilité aux lecteurs désirant appliquer ce concept. Une dernière chose que j'ai beaucoup apprecié dans ce livre est qu'il nous transporte du monde du technique abstrait vers un monde du pratique en prédisant le besoin des internautes.

Certains aspects techniques présentés par le livre peuvent causer la perte du fil conducteur pendant sa lecture tels que la présence de plusieurs diagrammes de classes et du code Java. J'aurais préféré que le livre ne traite pas les aspects conceptuels des choses à travers du technique. Ce même problème est rencontré par la présence de certaines formules mathématiques, J'aurais préféré que l'auteur propose des moyens faciles à mettre en œuvre pour aboutir à des résultats exploitables si c'est nécessaire de passer par des chiffres (calcul de la corrélation, similarité,…).

Ce que je n'ai pas aimé dans la méthodologie de l'application du data mining dans ce livre est le fait d'utiliser directement l'API Java de WEKA au lieu d'utiliser l'outil complet WEKA. Ceci, afin d'éviter de réinventer la roue. Aussi, l'auteur devrait normalement expliquer comment sélectionner les attributs de prédiction pour la construction d'un modèle prédictif.

English version :

In my opinion, this book is intended for people with good knowledge in Business Intelligence as well as Java to understand the examples provided, but this is a practical book for the application of collective intelligence in a web applications.

Being divided into three main parts, the book introduces us, through the 1st, 2nd and 3rd chapters, to the field of collective intelligence by giving us an overview of the architecture necessary to integrate collective intelligence in an application.

The 2nd part of the book deals with the intelligence derived from the collection of data. Through the 7th chapter, the reader is introduced to the process of data mining and its different types of algorithms. WEKA tool was used, an open source tool for data mining whose use will be more explicit in Chapter 9 through examples of clustering.

The last part deals with the application of intelligence in an application.

The strengths of the book lie in the methodology adopted by the author to explain how to apply collective intelligence in a web application. Thus, the book explains, through many examples, how to apply collective intelligence, by enumerating its profits in a web application. Each concept is illustrated by concrete examples. The author cites several notable sites using the concept of collective intelligence, something that may give more visibility to readers who wish to implement this concept. One last thing I greatly appreciated in this book is that it brings us from the world of abstract art to a world of practice in predicting the need for Internet users.

Some technical aspects presented by the book may cause the loss of the thread during playback, such as the presence of several class diagrams and Java code. I would have preferred that the book does not address the conceptual aspects of things through the technique. The same problem is encountered by the presence of some mathematical formulas, I would have preferred that the author offers easy ways to implement in order to achieve useful results if it is necessary to go by numbers (calculation of the correlation , similarity, ...).

What I did not like in the methodology of applying data mining in this book is the use of an API directly from Java WEKA instead of using WEKA tool . This is to avoid reinventing the wheel. Also, the author is expected to explain how to select attributes predictive for the construction of a predictive model.

Sommaire

  • Part 1 : GATHERING DATA FOR INTELLIGENCE
    • 1 Understanding collective intelligence
    • 2 Learning from user interactions
    • 3 Extracting intelligence from tags
    • 4 Extracting intelligence from content
    • 5 Searching the blogosphere
    • 6 Intelligent web crawling
  • Part 2 : DERIVING INTELLIGENCE
    • 7 Data mining: process, toolkits, and standards
    • 8 Building a text analysis toolkit
    • 9 Discovering patterns with clustering
    • 10 Making predictions
  • Part 3 : APPLYING INTELLIGENCE IN YOUR APPLICATION
    • 11 Intelligent search
    • 12 Building a recommendation engine

425 pages, 28/09/2008 Editions Manning Publications, ISBN10 : 1933988312
Commandez sur Manning Publications : 44.99  EUR TTC

Commandez sur www.amazon.fr :
35,32 EUR TTC (prix éditeur 44.99 EUR TTC) - Livraison Gratuite !

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