Abstract: In summary, the present invention concerns processes for computer supported processing of source data elements (32-40, 46-54) of a source data quantity (20) with the following steps: Input of at least one query data element, particularly a search string, Determination of a weighted link (42, 44, 60) of the query data element with at least one source data element (34, 38, 40) of the source data quantity (20), particularly with at least one hit string of the source data quantity (20) and Output of the at least one source data element (34, 38, 40) corresponding to a weight (w12, w13, w34) of the jointly weighted link (42, 44, 60) preferably of a hit likelihood of the query data element with the at least one source data element (34, 38, 40), wherein ?the weight (w12, w13, w34) of the weighted link (42, 44, and 60) is determined on the basis of at least one associative link (42, 44, and 60) as well as a further process, a system and a computer program product.

Abstract: A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability.

Abstract: A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability.