By Haralambos Marmanis, Dmitry Babenko
Web 2.0 purposes offer a wealthy consumer event, however the components you can't see are only as important-and outstanding. They use strong innovations to method info intelligently and supply good points according to styles and relationships in facts. Algorithms of the clever internet exhibits readers the right way to use an identical suggestions hired by way of family names like Google advert feel, Netflix, and Amazon to rework uncooked facts into actionable information.
Algorithms of the clever internet is an example-driven blueprint for developing purposes that acquire, learn, and act at the large amounts of information clients go away of their wake as they use the internet. Readers discover ways to construct Netflix-style suggestion engines, and the way to use an identical concepts to social-networking websites. See how click-trace research may end up in smarter advert rotations. the entire examples are designed either to be reused and to demonstrate a normal approach- an algorithm-that applies to a wide variety of scenarios.
As they paintings throughout the book's many examples, readers know about suggestion structures, seek and score, computerized grouping of comparable gadgets, class of gadgets, forecasting types, and self sufficient brokers. in addition they familiarize yourself with various open-source libraries and SDKs, and freely on hand APIs from the most popular websites on the net, corresponding to fb, Google, eBay, and Yahoo.
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Additional resources for Algorithms of the Intelligent Web
Does it have wizard-based workflows? Does it deal with free text? Does it involve reporting of any kind? Does it deal with geographic locations such as maps? Does our application provide search functionality? Do our users share content with each other? Is fraud detection important for our application? Is identity verification important for our application? Does our application make automated decisions based on rules? com> 12 CHAPTER 1 What is the intelligent web? This list is, of course, incomplete but it’s indicative of the possibilities.
A plethora of options is available for that purpose. We can’t review them all here, but we present four large categories that are fairly robust from a technology perspective, and are widely used. You should look into the literature for the specifics of your preferred method for collecting the addition data that you want to obtain. 1 Examine your functionality and your data You should start by identifying a number of use cases that would benefit from intelligent behavior. This will obviously differ from application to application, but you can identify these cases by asking some very simple questions, such as: ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ Does my application serve content that’s collected from various sources?
So, if we can’t make computers “think,” can we make them “learn”? Can we teach a computer to distinguish an animal based on its characteristics? How about a bad subprime mortgage application? How about something more complicated, such as recognizing your voice and replying in your native language—can a computer do that? The answer to these questions is a resounding yes. ” After all, you can always build a huge lookup table and get answers to your questions based on the data that you have in your database.