1. In a computer-based search system configured to receive a search query from a user and, in response to the search query, return documents or links to documents in a repository of documents to the user, a computer-implemented method of determining a user-interest model for the user, comprising the steps of: determining the repository of documents, the repository of documents comprising at least one of audio, video and textual documents associated with the user; determining interesting predicates within each of the documents based on machine learning; determining user-interest features for each class of the interesting predicates, the user-interest features comprising at least one of terms, phrases, and concepts related to the terms or phrases of interest to the user; determining user-interest identifying features for each conceptual abstraction of the documents; determining parsing features, the parsing features including at least one of parsing functions and parsing rules; determining transfer features, wherein the transfer features are associated with a count of the number of times a transfer function is applied to each of the documents; determining parameter weights for the user-interest features; filtering redundant features from the user-interest features; determining a probability distribution based on the user-interest features and the parameter weights; determining a user-interest model based on the user-interest features and the parameter weights: casting the user-interest features in a log-linear probability model; and estimating each associated parameter weight using L1-regularized maximum entropy estimation.