A framework for long‐term learning of topical user preferences in information retrieval

A framework for the long‐term learning of user preferences in information retrieval is presented. The multiple indexing and method‐object relations (MIMOR) model tightly integrates a fusion method and a relevance feedback processor into a learning model. Several black box matching functions can be combined into a linear combination committee machine which reflects the user's vague individual cognitive concepts expressed in relevance feedback decisions. An extension based on the soft computing paradigm couples the relevance feedback processor and the matching function into a unified retrieval system.