By Satnam Alag
There's loads of knowledge in a crowd, yet how do you hearken to 1000 humans speaking instantaneously? deciding on the wishes, wishes, and data of web clients may be like hearing a mob.
within the internet 2.0 period, leveraging the collective energy of consumer contributions, interactions, and suggestions is the major to industry dominance. a brand new classification of strong programming ideas permits you to detect the styles, inter-relationships, and person profiles-the collective intelligence--locked within the info humans go away at the back of as they surf web pages, submit blogs, and engage with different clients.
Collective Intelligence in motion is a hands-on guidebook for enforcing collective intelligence thoughts utilizing Java. it's the first Java-based booklet to stress the underlying algorithms and technical implementation of significant info amassing and mining suggestions like examining developments, studying relationships, and making predictions. It presents a practical method of personalization via combining content-based research with collaborative techniques.
This e-book is for Java builders enforcing Collective Intelligence in genuine, high-use functions. Following a operating instance during which you harvest and use info from blogs, you discover ways to strengthen software program so that you can embed on your personal functions. The code examples are instantly reusable and provides the Java developer a operating collective intelligence toolkit.
alongside the way in which, you're employed with, a few APIs and open-source toolkits together with textual content research and seek utilizing Lucene, web-crawling utilizing Nutch, and utilizing computer studying algorithms utilizing WEKA and the Java facts Mining (JDM) standard.
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Sample text
The Wisdom of Crowds. 2005. 0. 0#An_evolutionary_path_to_artificial_intelligence Learning from user interactions This chapter covers ■ Architecture for applying intelligence ■ Basic technical concepts behind collective intelligence ■ The many forms of user interaction ■ A working example of how user interaction is converted into collective intelligence Through their interactions with your web application, users provide a rich set of information that can be converted into intelligence. For example, a user rating an item provides crisp quantifiable information about the user’s preferences.
Let’s expand on our earlier definition of collective intelligence. Collective intelligence of users in essence is ■ ■ The intelligence that’s extracted out from the collective set of interactions and contributions made by your users. The use of this intelligence to act as a filter for what’s valuable in your application for a user—This filter takes into account a user’s preferences and interactions to provide relevant information to the user. 1—or it may be more involved—building models to recommend personalized content to a user.
6 There’s debate as to whether this intelligence will be attained by computers reasoning like humans or by sites leveraging the collective intelligence of humans using techniques such as collaborative filtering. Either way, having the dataset generated from real human interactions will be necessary and useful. In order to effectively leverage collective intelligence, you need to put the user at the center of your application, in essence building a user-centric application. 4 CHAPTER 1 Understanding collective intelligence Harnessing CI to transform from content-centric to user-centric applications Prior to the user-centric revolution, many applications put little emphasis on the user.