Abstract: A system and methods providing for the evaluation of transaction data records to first determine forward link associations between items as reference and related items identified by corresponding “expert” users as a basis for establishing expert database item association rules. The forward link associations are then evaluated to identify back link associations between reference and related items. Back link weights, corresponding to the respective back link associations reflecting the depth and strength of the back linked associations are determined and associated with the forward link associations to provide an augmented basis for subsequently evaluating the association rules collected into an expert database.

Abstract: A system and methods providing for the evaluation of transaction data records to first determine forward link associations between items as reference and related items identified by corresponding “expert” users as a basis for establishing expert database item association rules. The forward link associations are then evaluated to identify back link associations between reference and related items. Back link weights, corresponding to the respective back link associations reflecting the depth and strength of the back linked associations are determined and associated with the forward link associations to provide an augmented basis for subsequently evaluating the association rules collected into an expert database.

Abstract: A content and collaborative filtering system for recommending entertainment oriented content items, such as music and video, and other media content items to a user based on similarity in profile between the user and other users and between the content indexed in the user's profile and other content in the database. The system stores implicit and explicit ratings data for such content items provided by the users. Upon request of the user, the system accesses the user's profile and corresponding content interests database. The system uses the relationships between the content items to determine a subset of the content items to be referred to the user. The system also correlates a similarity between the user's ratings of the content items and other users' ratings. Based on the correlations, a subset of users is selected that is then used to provide recommendations to the user. The recommended items have a high probability of being subjectively appreciated by the user.