want to build two clusters from a certain cloud of points. As soon as we have identified two points each belonging to a different cluster, which is the next most informative point? If we take a point close to one we already know we can expect that it will likely belong to the same cluster. If we choose a point which is in between the two clusters, knowing which cluster it belongs to will help us in finding where the boundary is, allowing to classify many other points with just a few observations. The cold start problem is also exhibited by interface agents. Since such an agent typically learns the user's preferences implicitly by observing patterns in the user's behaviour – "watching over the shoulder" – it would take time before the agent may perform any adaptations personalised to the user. Even then, its assistance would be limited to activities which it has formerly observed the user engaging in. The cold start problem may be overcome by introducing an element of collaboration amongst agents assisting various users. This way, novel situations may be handled by requesting other agents to share what they have already learnt from their respective users. Feature mapping In recent years more advanced strategies have been proposed, they all rely on machine learning and attempt to merge the content and collaborative information in a single model. One example of this approaches is called attribute to feature mapping which is tailored to matrix factorization algorithms. The basic idea is the following. A matrix factorization model represents the user-item interactions as the product of two rectangular matrices whose content is learned using the known interactions via machine learning. Each user will be associated to a row of the first matrix and each item with a column of the second matrix. The row or column associated to a specific user or item is called latent factors. When a new item is added it has no associated latent factors and the lack of interactions does not allow to learn them, as it was done with other items. If each item is associated to some features (e.g. author, year, publisher, actors) it is possible to define an embedding function, which given the item features estimates the corresponding item latent factors. The embedding function can be designed in many ways and it is trained with the data already available from warm items. Alternatively,