From then on, the system improved: between 2011 and 2012, the platform incorporated classifications to identify racist or violent videos and avoid their recommendation, and began to take viewing time into account as a recommendation factor, helping to improve the quality of the system. Similarly, between 2015 and 2017, YouTube downgraded sensationalist content to avoid its recommendation and reduce misinformation.
On the other hand, it also began to implement a machine learning system to help deploy content to sensitive groups, such as underage users, and a predictive system was also incorporated to avoid recommending videos that may pose risks to them, such as challenges. All of this eventually gave way to the recommendation system that the platform currently has.
The idea of recommendations is to provide users with a list of belarus business email database content that is of interest to them or that has some associated value for them. To achieve this, the video social network takes into account users' viewing habits and compares them with those of other users with similar habits. Thus, the system uses this information to suggest content that may be of interest to users. For example, if we like videos about digital marketing and the system notices that most users who watch the same type of videos also watch videos about technological developments, it is very likely that it will start recommending these videos to us.
However, privacy is also an important factor in ensuring a better browsing experience for users. Therefore, YouTube also has functions that allow you to control the platform's access to viewing history information. In this way, users can delete content from their history, clear their history completely, or even pause their tracking , whether it be search or viewing history.