By Haralambos Marmanis, Dmitry Babenko
Web 2.0 functions supply a wealthy person event, however the components you can't see are only as important-and outstanding. They use strong options to method info intelligently and supply beneficial properties in keeping with styles and relationships in information. Algorithms of the clever net exhibits readers the best way to use an analogous concepts hired by way of loved ones names like Google advert feel, Netflix, and Amazon to remodel uncooked info into actionable information.
Algorithms of the clever internet is an example-driven blueprint for growing functions that acquire, research, and act at the sizeable amounts of information clients go away of their wake as they use the internet. Readers learn how to construct Netflix-style suggestion engines, and the way to use a similar ideas to social-networking websites. See how click-trace research may end up in smarter advert rotations. all of the examples are designed either to be reused and to demonstrate a normal process- an algorithm-that applies to a large diversity of scenarios.
As they paintings throughout the book's many examples, readers find out about suggestion platforms, seek and rating, computerized grouping of comparable gadgets, class of gadgets, forecasting types, and self reliant brokers. additionally they familiarize yourself with lots of open-source libraries and SDKs, and freely to be had APIs from the most popular websites on the web, reminiscent of fb, Google, eBay, and Yahoo.
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Extra resources for Algorithms of the Intelligent Web
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?
In the case of a restaurant search engine, you want to assess how good a restaurant is based on reviews from people who ate there. In some cases, ratings may be available, but most of the time these reviews are plain, natural language, text. Reading the reviews one-by-one and ranking the restaurants accordingly is clearly not a scalable business solution. Intelligent techniques can be employed during screen scraping and help you automatically categorize the reviews and assess the ranking of the restaurants.
Lucene analyzers provide a wealth of capabilities, such as the ability to add synonyms, modify stop words (words that are explicitly removed from the text before indexing), and deal with non-English languages. We’ll use Lucene analyzers throughout the book, even in chapters that don’t deal with search. The general idea of identifying the unique characteristics of a text description is crucial when we deal with documents. Thus, analyzers become very relevant in areas such as the development of spam filters, recommendations that are based on text, enterprise, or tax compliance applications, and so on.