Abstract: Apparatuses and methods are provided for classifying textual content using a text classifier for determining to which class the textual content belongs. After classification, the text classifier provides the classification result and a context relevant to the classification result to an explanation system. The explanation system predicts, from the classification result and the context relevant to the classification result, one or more reasons behind the classification result.

Abstract: There is provided a client adapted for generating personalized cold-start federated recommendations for a user of the client. The client generates personalized recommendations for three cold-start scenarios, namely i) recommendation of an item to a new user which does not have any history of user-item interactions, ii) recommendation of a new item to a set of the most prospective users where the item has no history, and iii) recommendation of a new item to a new user, where there is no history associated with either the user or the item. The client uses a federated multi-view matrix factorization method to generate cold-start recommendations without transferring users' personal data to a remote server. Further, a server and a content provider for assisting in generating the personalized cold-start recommendations are provided in a federated set-up according to some aspects.