By Satnam Alag
There's loads of knowledge in a crowd, yet how do you hearken to 1000 humans conversing immediately? deciding upon the desires, wishes, and data of net clients should 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 marketplace dominance. a brand new type of strong programming suggestions helps you to observe the styles, inter-relationships, and person profiles-the collective intelligence--locked within the facts humans depart in the back of as they surf web pages, put up blogs, and have interaction with different clients.
Collective Intelligence in motion is a hands-on guidebook for enforcing collective intelligence strategies utilizing Java. it's the first Java-based e-book to stress the underlying algorithms and technical implementation of important info amassing and mining recommendations like studying tendencies, studying relationships, and making predictions. It offers a practical method of personalization through combining content-based research with collaborative ways.
This booklet is for Java builders imposing Collective Intelligence in actual, high-use purposes. Following a working instance during which you harvest and use details from blogs, you discover ways to increase software program so you might embed on your personal purposes. 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 couple of APIs and open-source toolkits together with textual content research and seek utilizing Lucene, web-crawling utilizing Nutch, and employing desktop studying algorithms utilizing WEKA and the Java information Mining (JDM) standard.
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Additional info for Collective Intelligence in Action
Web applications that take this approach develop deeper relationships with their users, provide more value to users who return more often, and ultimately offer more targeted experiences for each user according to her personal need. 3 4 CHAPTER 1 Understanding collective intelligence Web users are undergoing a transformation. Users are expressing themselves. This expression may be in the form of sharing their opinions on a product or a service through reviews or comments; through sharing and tagging content; through participation in an online community; or by contributing new content.
With a new blog being created virtually every second, the blogosphere is an important source of information that can be leveraged in your application. People write blogs on virtually every topic. Next, let’s look at the third category of intelligence, which is derived from analyzing the data collected. 3 Derived intelligence This section deals with information derived from the data you collect from users. Here are a few examples of techniques and features that deal with derived intelligence. DATA AND TEXT MINING The process of finding patterns and trends that would otherwise go undetected in large datasets using automated algorithms is known as data mining.
Analyze content to build user profiles Analyze content associated with a user to extract keywords. This information is used to build user profiles. Clustering and predictive models Cluster users and items, build predictive models. Recommendation engines Recommend related content or users based on intelligence gathered from user interaction and analyzing content. Search Show more pertinent search results using a user’s profile. Harness external content Provide relevant information from the blogosphere and external sites John and Jane showed us a few nice things to apply to their site, but there are other benefits of applying collective intelligence to your application.