Are differences. I'll explain to you why. On machine learning platforms like netflix use all the information they can get from their users. Starting from the content they offer, they can link it to the interaction we have with it, if we consume one or several styles of content (we are into intrigue series, gastronomy, documentaries about visionary people...), if there is a consumption habit that characterizes us (perhaps we are sporadic consumers, or we tend to watch content on the weekend, or perhaps we are into series marathons...), the moment or moments of the day in which we connect.
All this is usable information and it forms a profile, which can be connected with the activity of users similar to us in tastes, habits and preferences. A recommendation engine is nothing more than an intelligent algorithm that analyzes the information e commerce photo editing service about our tastes and habits and, based on this, proposes content similar to what we have already seen. Additionally, you can propose content seen by other people who, due to tastes, habits and preferences, are similar to us. The foregoing, in addition to facilitating the user experience and ensuring that it is satisfactory, favors in a certain way maintaining an active consumption of content since the
perception of users is that "there are always interesting things to see" and subscriptions are renewed. . This has a favorable impact on the income statement. For curious minds, in this article “ how the netflix recommendation system works ”, you can delve into the recommendation algorithm that netflix uses. Beyond your preferences however, netflix is always one or two steps ahead. One of the latest innovations that the content company incorporates into its recommendation system is the customization of the covers of movies, documentaries or series. Perhaps you