Abstract: A method and an apparatus to perform feature similarity mapping are presented. In one embodiment, the method includes mapping a set of data items onto a feature similarity matrix (FSM) in a feature similarity system. The FSM has multiple dimensions (generally ten or more). Each item has a number of features and each of the features maps to a distinct matrix node weight. The method may further include positioning of data in the FSM, the position of data corresponding to one or more items having one or more features similar to one or more of the features of the items mapped to FSM nodes in close proximity.

Abstract: A method and an apparatus to perform feature weighted data search and recommendation are presented. A process for search and recommendation is driven by agents with features. The process may determine a recommended set of items out of a population of items based on a single sample item or on a sample set of items. The process starts by performing an analysis on a single item or on a set of items. A new set of agents is created by adapting existing agents based on the results of an analysis. A search and recommendation among the population of items is guided by an agent. An agent is adapted according to user feedback. A new agent is created based on a combination of several adapted agents to include the best features of each. Newly created agents are employed to determine recommendations from a population of items by comparing a similarity between an item and an agent. The recommendations are presented to a user through a user interface.