This paper has studied and observed the consumer behavior by collecting all kinds of actions of consumers and applying the ubiquitous environment which are RFID and camera sensors to gathering log data. The consumer's behavior was automatically saved as a log file and analyzed by using artificial neural network. From neural network model allows us to categorize theconsumers into 3 groups which are: A) the consumers who were certain in buying a product, B) the consumers who intended to buy a product but could not decide what to buy, and C) the consumers who did not intend to buy but stop by to view the product.
This paper has proposed a shopping assistance service. We provide consumer-friendly services which based on personal behavior log data. The consumer-friendly services are information service which display through a monitor, robot, sound and music that can attract new consumer. In this paper, we study personal behavior from a log file that has been recorded by RFID and camera sensors in the ubiquitous environment. 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 collaborative filtering to retrieve information. Finally, we will also propose a ubiquitous shop space experiment.
Nowadays more people have started using their mobile phone to access information they need from anywhere at anytime. In advanced mobile technology, Location Service allows users to quickly pinpoint their location as well as makes a recommendation to fascinating events. However, users desire more appropriate recommendation services. In other words, the message service should push a message at a proper place in time. In consequence, customers obtain a higher level of satisfaction. In this paper, we propose a framework of time, place, purpose and personal profile based recommendation service. We illustrate scenarios in "push", "pull" and "don't disturb" services, where our DB queries can recommend the relevant message to users. The three factors: time, place and purpose are mutually dependent and the basic rules to analyze the essential data are summarized. We also create algorithms for DB query. We are filtering messages by one important factor: personal profile such as user's preference and degree of preference. Furthermore, we discuss an implementation of the prototype system, including results of experimental evaluation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.