TY - GEN
T1 - Discovering user behavior patterns in personalized interface agents
AU - LIU, Jiming
AU - WONG, Kelvin C K
AU - Hui, Ka Keung
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2000
Y1 - 2000
N2 - In human-computer interaction, user interface events can be recorded and organized into sequences of episodes. By computing their implication networks, episode frequencies, and some heuristic measures of interestingness, we can readily derive some application-specific episode association rules. In order to demonstrate the proposal method, we have developed a personalized interface agent that can take into consideration interface events in analyzing user goals. It can then delegate on behalf of the user to interact with the software based on the recognized plans. In order to adapt to different users’ needs, the agent can personalize its assistance by learning user profiles. Currently, we have used the Microsoft Word as a test case. By detecting and analyzing the patterns of user behavior in using Word, the agent can automatically assist the users in certain Word tasks. The pattern association can be achieved at several levels, i.e., text-level (phrase association), paragraphlevel (formatting association), and document-level (style and source association).
AB - In human-computer interaction, user interface events can be recorded and organized into sequences of episodes. By computing their implication networks, episode frequencies, and some heuristic measures of interestingness, we can readily derive some application-specific episode association rules. In order to demonstrate the proposal method, we have developed a personalized interface agent that can take into consideration interface events in analyzing user goals. It can then delegate on behalf of the user to interact with the software based on the recognized plans. In order to adapt to different users’ needs, the agent can personalize its assistance by learning user profiles. Currently, we have used the Microsoft Word as a test case. By detecting and analyzing the patterns of user behavior in using Word, the agent can automatically assist the users in certain Word tasks. The pattern association can be achieved at several levels, i.e., text-level (phrase association), paragraphlevel (formatting association), and document-level (style and source association).
UR - http://www.scopus.com/inward/record.url?scp=84944050860&partnerID=8YFLogxK
U2 - 10.1007/3-540-44491-2_58
DO - 10.1007/3-540-44491-2_58
M3 - Conference contribution
AN - SCOPUS:84944050860
SN - 3540414509
SN - 9783540414506
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 398
EP - 403
BT - Intelligent Data Engineering and Automated Learning - IDEAL 2000
A2 - Leung, Kwong Sak
A2 - Chan, Lai-Wan
A2 - Meng, Helen
PB - Springer Verlag
T2 - 2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000
Y2 - 13 December 2000 through 15 December 2000
ER -