Evolutionary algorithm based adaptive navigation in information retrieval interfaces

In computer interfaces in general, especially in information retrieval tasks, it is important to be able to quickly find and retrieve information. State of the art approach, used, for example, in search engines, is not effective as it introduces losses of meanings due to context to keywords back and forth translation. Authors argue it increases the time and reduces the accuracy of information retrieval compared to what it could be in the system that employs modern information retrieval and text mining methods while presenting results in an adaptive human- computer interface where system effectively learns what operator needs through iterative interaction. In current work, a combination of adaptive navigational interface and real time collaborative feedback analysis for documents relevance weighting is proposed as an viable alternative to prevailing "telegraphic" approach in information retrieval systems. Adaptive navigation is provided through a dynamic links panel controlled by an evolutionary algorithm. Documents relevance is initially established with standard information retrieval techniques and is further refined in real time through interaction of users with the system. Introduced concepts of multidimensional Knowledge Map and Weighted Point of Interest allow finding relevant documents and users with common interests through a trivial calculation. Browsing search approach, the ability of the algorithm to adapt navigation to users interests, collaborative refinement and the self-organising features of the system are the main factors making such architecture effective in various fields where non-structured knowledge shall be represented to the users.