In the recommendation space, the user can view and select various plans actively. The recommendation space consists of two dimensions; the preference reduction axis and the preference extension axis, in that, various plans are arranged in a plane.
Published in Chapter:
Awareness-Based Recommendation by Passively Interactive Learning: Toward a Probabilistic Event
Tomohiro Yamaguchi (National Institute of Technology, Nara College, Japan), Takuma Nishimura (Nippon Telegraph and Telephone West Corporation, Japan), Shota Nagahama (National Institute of Technology, Nara College, Japan), and Keiki Takadama (The University of Electro-Communications, Japan)
Copyright: © 2019
|Pages: 29
DOI: 10.4018/978-1-5225-5276-5.ch009
Abstract
In artificial intelligence and robotics, one of the important issues is to design human interface. There are two issues: One is the machine-centered interaction design. Another one is the human-centered interaction design. This research aims at the latter issue. This chapter presents the interactive learning system to assist positive change in the preference of a human toward the true preference. Then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users. As future research directions, a probabilistic event and its basic recommendation way are discussed.