The present work deals with the the recommendation of values in interactive configuration, with no prior knowledge about the user, but given a list of products previously configured and bought by other users ("sale histories"). The basic idea is to recommend, for a given variable at a given step of the configuration process, a value that has been chosen by other users in a similar context, where the context is defined by the variables that have already been decided, and the values that the current user has chosen for these variables. From this point, two directions have been explored. The first one is to select a set of similar configurations in the sale history (typically,the k closest ones, using a distance measure) and to compute the best recommendation from this set - this is the line proposed by [9]. The second one, that we propose here, is to learn a Bayesian network from the entire sample as model of the users’ preferences, and to use it to recommend a pertinent value.