K-DIME: An Adaptive System to Retrieve Images from the WEB Using Subjective Criteria

Abstract

Researchers in the field of Database Systems are bound to expand database techniques beyond traditional areas, particularly to the realm of the web. With the growing importance of information technology in today’s industry, new types of applications are appearing that require a broader understanding of information. Recently, many efforts have been made to understand and model the subjective factors that play an important role in our social life and communication so that they could be embedded into new information technologies. We propose here our framework for endowing a software agent with the capability to personalize itself to its user through interaction and perform tasks that involve subjective parameters. We describe K-DIME, a software prototype that can retrieve material from the Web on the basis of both objective and subjective features of the content. K-DIME allows users to create their own Kansei User Model. With its ability to bootstrap a new user model from the model of a user with similar profile, it significantly reduces the workload generally associated with an online learning phase. Continuous adaptation driven by specific patterns of interaction with the user enables K-DIME to cope with the intrinsic variability of subjective impressions. A working prototype has been implemented and applied in a scenario in which users are asked to identify pictures they would like to display on a greeting card.