Abstract: A content recommender based on collaborative filtering and implicit user feedbacks comprising retrieving a social graph split into a user and the user's relationship network in order to obtain a social aware model of the user's preferences based on preferences of the users belonging to the user's relationship network, minimizing the objective function for all the response values of the whole user-item matrix, the response values meaning implicit and explicit feedback data, providing a list of content recommendations obtained by a score function computed using the social aware model.

Abstract: A method and system of optimizing a ranked list (5) of recommended items that is based in a multidimensional data set (2) comprising context-aware information about the of a plurality of users and a plurality of items. A mathematical recommendation model (3) is trained with the multidimensional data set (2) by applying a smooth objective function that allows the use of fast optimizing algorithm and that quantifies the relevance of the ranked lists provided by an optimization algorithm.

Abstract: A method and system of optimizing a ranked list (5) of recommended items that is based in a multidimensional data set (2) comprising context-aware information about the of a plurality of users and a plurality of items. A mathematical recommendation model (3) is trained with the multidimensional data set (2) by applying a smooth objective function that allows the use of fast optimising algorithm and that quantifies the relevance of the ranked lists provided by an optimization algorithm.

Abstract: Method for Context-Aware Collaborative Filtering comprising: a) performing collaborative filtering introducing a user-item-context interaction as a definition of the data and modelling them using tensor factorization (TF); b) generating one or more recommendations using said modelling; and c) displaying the recommendations to a user. said tensor used for tensor Factorization (TF) represent indirect indications of a user's preferences for an item, meaning implicit feedback data.

Abstract: It comprises computing values of cognitive and personality indicators of users of a telecom operator by means of machine learning and data mining algorithms from information available in a telecom operator system extracted from Social Network Analysis metrics, Call Detailed Record information and commercial information of said users stored in an operator's Data Warehouse and Customer Relationship Management systems as well as information from previous surveys, or questionnaires, answered by a representative sample of users as an input of said machine learning and said data mining algorithms. The method involves building a complex computer model that infers the values of the psychological dimensions of said users by means of said machine learning and said data mining algorithms to obtain a multi-dimensional vector for each of the users.