There are many different types of recommender systems as they tend to be designed with a very specific purpose in mind, such as music suggestions or movie suggestions. However, the basic concept is still the same – that a user’s likes and dislikes can be accurately gauged by looking at past data.
To predict what somebody will like in this way seems to be an easy task, however, it’s actually deceptively complex. This complexity is why the field of recommender systems has only started to blossom now that we are well and truly in the computer age.
The ability to accurately predicting user preferences is something that’s extremely attractive to businesses as it helps to drive efficiency and, in particular, can help to personalize marketing efforts. Because of this, it is a branch of AI that’s growing rapidly and becoming a crucial element of many companies’ marketing strategies.