We have been developing an experimental prototype system - Ubiquitous Shop Space, an application of personal information assistance in a shopping context, (1) to find items of users’ interest and/or preferences through data observed from users’ behaviors in a ubiquitous information environment, (2)to automatically build a user preference model, and (3) to apply the model and provide suitable information service in the real world. In this paper, we study personal behavior from a log file that has been recorded by RFID and camera sensors. Utilizing ubiquitous sensors, we apply three types of observation scopes to model each user’s preference. They are microscopic, mezzoscopic, and macroscopic observation scopes. We have proposed the behavior analysis, algorithms, and combined content-based and collaborative filtering to retrieve information. We provide consumer-friendly services which are based on personal behavior log data. Our system can inject suitable and controlled messages to a user to build up his precise preference model without putting any stress on him.