to provide some preferences to build an initial user profile. A threshold has to be found between the length of the user registration process, which if too long might indice too many users to abandon it, and the amount of initial data required for the recommender to work properly. Similarly to the new items case, not all recommender algorithms are affected in the same way. Item-item recommenders will be affected as they rely on user profile to weight how relevant other user's preferences are. Collaborative filtering algorithms are the most affected as without interactions no inference can be made about the user's preferences. User-user recommender algorithms behave slightly differently. A user-user content based algorithm will rely on user's features (e.g. age, gender, country) to find similar users and recommend the items they interacted with in a positive way, therefore being robust to the new user case. Note that all these information is acquired during the registration process, either by asking the user to input the data himself, or by leveraging data already available e.g. in his social media accounts. Mitigation strategies Due to the high number of recommender algorithms available as well as system type and characteristics, many strategies to mitigate the cold-start problem have been developed. The main approach is to rely on hybrid recommenders, in order to mitigate the disadvantages of one category or model by combining it with another. All three categories of cold-start (new community, new item, and new user) have in common the lack of user interactions and presents some commonalities in the strategies available to address them. A common strategy when dealing with new items is to couple a collaborative filtering recommender, for warm items, with a content-based filtering recommender, for cold-items. While the two algorithms can be combined in different ways, the main drawback of this method is related to the poor recommendation quality often exhibited by content-based recommenders in scenarios where it is difficult to provide a comprehensive description of the item characteristics. In case of new users, if no demographic feature is present or their quality is too poor, a common strategy is to offer them non-personalized recommendations. This means that they could be recommended simply the most popular items either globally or for their specific geographical region or language. Profile completion One of the available options when dealing with cold users or items is to rapidly acquire some preference data.