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Today the user is inundated with data and information and it is estimated that the amount of data produced each day is equivalent to 2.5 trillion bytes (1018) and that more than 90% of the total data available today was produced in the last two years alone (Zikopoulos & Eaton, 2011).
Therefore, the user has difficulty in choosing his content for example:
Which movie to watch? Which product to buy? Which book to download? Etc. Moreover, the size of these domains are massive so faced with these scourge large companies such as Amazon, Netflix, YouTube etc... Moreover, researchers have begun to take a close interest in order to find a solution to the exponential increase in data.
ex: Netflix (Bennett & Lanning, 2007) which has more than one hundred million hours of viewing, which makes it very difficult to choose their content. That is why they organized a competition to create a recommendation system adapted to their platforms and the reward was 1 million dollars!
Recommendation System
The Recommendation System is a program that helps users without sufficient personal experience to make a choice from a set of items. In addition, that tries to predict articles (movies, music, books, news, and web pages) that a user would be interested in and draw his profile. Often, collaborative filtering algorithms execute this function.