Designing a Machine Learning System - Machine Learning with Spark - page 42

Personalization
Perhaps one of the most important potential applications of machine learning in
MovieStream's business is personalization. Generally speaking, personalization refers to
adapting the experience of a user and the content presented to them based on various
factors, which might include user behavior data as well as external factors.
Recommendations are essentially a subset of personalization. Recommendation generally
refers to presenting a user with a list of items that we hope the user will be interested in.
Recommendations might be used in web pages (for example, recommending related
products), via e-mails or other direct marketing channels, via mobile apps, and so on.
Personalization is very similar to recommendations, but while recommendations are usu-
ally focused on an explicit presentation of products or content to the user, personalization is
more generic and, often, more implicit . For example, applying personalization to search on
the MovieStream site might allow us to adapt the search results for a given user, based on
the data available about that user. This might include recommendation-based data (in the
case of a search for products or content) but might also include various other factors such
as geolocation and past search history. It might not be apparent to the user that the search
results are adapted to their specific profile; this is why personalization tends to be more im-
plicit.