There are various ways to do that depending on the amount of information required. These techniques are called preference elicitation strategies. This may be done either explicitly (by querying the user) or implicitly (by observing the user's behaviour). In both cases, the cold start problem would imply that the user has to dedicate an amount of effort using the system in its 'dumb' state – contributing to the construction of their user profile – before the system can start providing any intelligent recommendations. For example MovieLens, a web-based recommender system for movies, asks the user to rate some movies as a part of the registration. While preference elicitation strategy are a simple and effective way to deal with new users, the additional requirements during the registration will make the process more time-consuming for the user. Moreover, the quality of the obtained preferences might not be ideal as the user could rate items he/she has seen months or years ago or the provided ratings could be almost random if the user provided them without paying attention just to complete the registration quickly. The construction of the user's profile may also be automated by integrating information from other user activities, such as browsing histories or social media platforms. If, for example, a user has been reading information about a particular music artist from a media portal, then the associated recommender system would automatically propose that artist's releases when the user visits the music store. A variation of the previous approach is to automatically assign ratings to new items, based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items' content-based characteristics. It is also possible to create initial profile of a user based on the personality characteristics of the user and use such profile to generate personalized recommendation. Personality characteristics of the user can be identified using a personality model such as five factor model (FFM). Another of the possible techniques is to apply active learning (machine learning). The main goal of active learning is to guide the user in the preference elicitation process in order to ask him to rate only the items that for the recommender point of view will be the most informative ones. This is done by analysing the available data and estimating the usefulness of the data points (e.g., ratings, interactions). As an example, say that we