1. A computer-implemented method for providing information based on automatically determined relationships, the method comprising: under control of one or more computing systems configured to provide a relevance determination service, automatically determining relevant information to recommend by, automatically analyzing contents of a plurality of documents related to a first domain of interest to identify multiple inter-term relationships between at least some of a plurality of terms that are present in the contents of the documents, each of the identified relationships indicating an initial assessed relevance between at least one of the terms and at least one other of the terms; automatically generating a term relevance neural network that models the assessed relevances of the identified relationships, the term relevance neural network initially modeling the assessed initial relevances, and repeatedly updating the assessed relevances that are modeled by the term relevance neural network based on feedback obtained from users that perform selections corresponding to the plurality of terms; automatically generating a probabilistic Bayesian network based on the updated assessed relevances of at least some of the identified relationships, the probabilistic Bayesian network including information that indicates probabilities for relationships between at least some of the plurality of terms; and using the information included in the probabilistic Bayesian network to provide recommendations related to the first domain by, for each of multiple users: obtaining information about a first group of one or more of the plurality of terms for which the user has expressed a preference; for each of one or more target terms of the plurality of terms that are not in the first group, automatically determining a probability that the target term is an unexpressed preference of the user, the determined probability being based on the preference of the user for the one or more terms of the first group and being based on one or more relationships between the one or more terms of the first group and the target term that are indicated in the information included in the probabilistic Bayesian network; and providing one or more recommendations for the user related to the first domain that are based on a selected second group of at least one of the target terms, the target terms of the second group being selected based on the determined probabilities that those target terms are unexpressed preferences of the user, and wherein the target terms of the selected second group for at least one of the multiple users differ from the target terms of the selected second group for at least one other of the multiple users.