Discovering user behavior patterns in personalized interface agents

Jiming Liu; Kelvin Chi Kuen Wong; Ka Keung Hui

doi:10.1007/3-540-44491-2_58

Discovering user behavior patterns in personalized interface agents

Jiming Liu, Kelvin Chi Kuen Wong, Ka Keung Hui

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

3 Citations (Scopus)

Abstract

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).

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2000
Subtitle of host publication	Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings
Editors	Kwong Sak Leung, Lai-Wan Chan, Helen Meng
Publisher	Springer Verlag
Pages	398-403
Number of pages	6
ISBN (Print)	3540414509, 9783540414506
DOIs	https://doi.org/10.1007/3-540-44491-2_58
Publication status	Published - 2000
Event	2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000 - Hong Kong, Hong Kong Duration: 13 Dec 2000 → 15 Dec 2000 https://link.springer.com/book/10.1007/3-540-44491-2 (Conference Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1983
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000
Country/Territory	Hong Kong
City	Hong Kong
Period	13/12/00 → 15/12/00
Internet address	https://link.springer.com/book/10.1007/3-540-44491-2 (Conference Proceedings)

Access to Document

10.1007/3-540-44491-2_58

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Liu, J., Wong, K. C. K., & Hui, K. K. (2000). Discovering user behavior patterns in personalized interface agents. In K. S. Leung, L.-W. Chan, & H. Meng (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings (pp. 398-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1983). Springer Verlag. https://doi.org/10.1007/3-540-44491-2_58

Liu, Jiming ; Wong, Kelvin Chi Kuen ; Hui, Ka Keung. / Discovering user behavior patterns in personalized interface agents. Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings. editor / Kwong Sak Leung ; Lai-Wan Chan ; Helen Meng. Springer Verlag, 2000. pp. 398-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Discovering user behavior patterns in personalized interface agents",

abstract = "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{\textquoteright} 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).",

author = "Jiming Liu and Wong, {Kelvin Chi Kuen} and Hui, {Ka Keung}",

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year = "2000",

doi = "10.1007/3-540-44491-2_58",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Liu, J, Wong, KCK & Hui, KK 2000, Discovering user behavior patterns in personalized interface agents. in KS Leung, L-W Chan & H Meng (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1983, Springer Verlag, pp. 398-403, 2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000, Hong Kong, Hong Kong, 13/12/00. https://doi.org/10.1007/3-540-44491-2_58

Discovering user behavior patterns in personalized interface agents. / Liu, Jiming; Wong, Kelvin Chi Kuen; Hui, Ka Keung.
Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings. ed. / Kwong Sak Leung; Lai-Wan Chan; Helen Meng. Springer Verlag, 2000. p. 398-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1983).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Hui, Ka Keung

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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).

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ER -

Liu J, Wong KCK, Hui KK. Discovering user behavior patterns in personalized interface agents. In Leung KS, Chan LW, Meng H, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents - 2nd International Conference, Proceedings. Springer Verlag. 2000. p. 398-403. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44491-2_58

Discovering user behavior patterns in personalized interface agents

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