one could apply a group-specific method. A group-specific method further decomposes each latent factor into two additive parts: One part corresponds to each item (and/or each user), while the other part is shared among items within each item group (e.g., a group of movies could be movies of the same genre). Then once a new item arrives, we can assign a group label to it, and approximates its latent factor by the group-specific part (of the corresponding item group). Therefore, although the individual part of the new item is not available, the group-specific part provides an immediate and effective solution. The same applies for a new user, as if some information is available for them (e.g. age, nationality, gender) then his/her latent factors can be estimated via an embedding function or a group-specific latent factor. Hybrid feature weighting Another recent approach which bears similarities with feature mapping is building a hybrid content-based filtering recommender in which features, either of the items or of the users, are weighted according to the user's perception of importance. In order to identify a movie that the user could like, different attributes (e.g. which are the actors, director, country, title) will have different importance. As an example consider the James Bond movie series, the main actor changed many times during the years, while some did not, like Lois Maxwell. Therefore, her presence will probably be a better identifier of that kind of movie than the presence of one of the various main actors. Although various techniques exist to apply feature weighting to user or item features in recommender systems, most of them are from the information retrieval domain like tf–idf, Okapi BM25, only a few have been developed specifically for recommenders. Hybrid feature weighting techniques in particular are tailored for the recommender system domain. Some of them learn feature weight by exploiting directly the user's interactions with items, like FBSM. Others rely on an intermediate collaborative model trained on warm items and attempt to learn the content feature weights which will better approximate the collaborative model. Many of the hybrid methods can be considered special cases of factorization machines. Differentiating regularization weights The above methods rely on affiliated information from users or items. Recently, another approach mitigates the cold start problem by assigning lower constraints to the latent factors associated with the items or users that reveal more information (i.e., popular items and active users),