User:Samiri1512/sandbox

Intelligent Web Algorithms is a search method that use Artificial Intelligence (AI), that many people have encountered while using the Web. As you navigate the Web using a social networking site or browsing the news, you are leaving a personal trail which Intelligent Web Algorithms utilizes. Have you ever searched for a product online, perhaps say something on Amazon and then ventured off to a social networking site to see that same product turning up in advertisement off in the margins? Or have you watched certain movies on Netflix, and then get recommendations based on what you have previously watched? That is Intelligent Web Algorithms in action. Web Intelligence Algorithm is an advanced machine learning system based on information which is discovered in the Web environment, and processes this information to create a personalized web experience. These algorithms sift, select, compare, aggregate, and correct data, to decide what matters by following a simple yet powerful set of rules. Companies such as Google, Netflix, and Amazon use Intelligent Web Algorithms. A notable example of an Intelligent Web Algorithm is Google's PageRank Algorithm, named after Larry Page, one of Google’s founders.

The way Intelligent Web Algorithms are designed to personalize a user's experiences is that it firsts sorts data collected based on what the user has searched for, this information which is then indexed by the algorithm using a traditional information retrieval technique. In order for the Intelligent Web Algorithm to be successful it must be able to retrieve and identify information considered “useful” to the user. Then next process of a Web Intelligent Algorithm is click analysis. This component of the algorithm learns the preferences of a user toward a particular site or topic, and therefore can generate a personalized experiences for the user. The algorithm then generates two broad categories based on the data collected for creating recommendations—collaborative filtering and the content-based approach. After the data is sorted in theses two categories, it classifies the information to derive a new conclusion in order to generate more recommendations based on the users activities. The algorithm can generate new conclusions for a more personalized Web experience, therefore predicting and predetermining what an individual may search for in the future, and generating a list of recommendations for them.