Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Attentive systems attend to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad - ranging from human butlers to web sites that profile users - we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe Simple User Interest Tracker (Suitor), an architecture for developing attentive information systems that track computer users through multiple channels - eye gaze, web browsing, application use, to determine interests and to try to satisfy information needs. By observing behavior and modeling users, Suitor can be used to find and display potentially relevant information that is both timely and non-disruptive to the user's ongoing activities.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Ran Iwamoto, Kyoko Ohara
ICLC 2023
Susan L. Spraragen
International Conference on Design and Emotion 2010
Zhikun Yuen, Paula Branco, et al.
DSAA 2023