XCS for Personalizing Desktop Interfaces
We investigate whether XCS, a genetic algorithm based learning classifier system, can harness information from a user's environment to help desktop applications better personalize themselves to individual users. Specifically, we evaluate XCSs ability to predict user-preferred actions for a cale...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2010-08, Vol.14 (4), p.547-560 |
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| container_title | IEEE transactions on evolutionary computation |
| container_volume | 14 |
| creator | Shankar, Anil Louis, Sushil J |
| description | We investigate whether XCS, a genetic algorithm based learning classifier system, can harness information from a user's environment to help desktop applications better personalize themselves to individual users. Specifically, we evaluate XCSs ability to predict user-preferred actions for a calendar and a media player. Results from three real-world user studies indicate that XCS significantly outperforms a decision-tree learner to successfully predict user preferences for these two desktop interfaces. Our results also show that removing external user-related contextual information degrades XCSs performance. This performance degradation emphasizes the need for desktop applications to access external contextual information to better learn user preferences. Our results highlight the potential for a learning classifier systems based approach for personalizing desktop applications to improve the quality of human-computer interaction. |
| doi_str_mv | 10.1109/TEVC.2009.2021466 |
| format | Article |
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Our results highlight the potential for a learning classifier systems based approach for personalizing desktop applications to improve the quality of human-computer interaction.</description><subject>Application software</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Calendars</subject><subject>Classifiers</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Data mining</subject><subject>Decision-trees</subject><subject>Degradation</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Exact sciences and technology</subject><subject>Field programmable gate arrays</subject><subject>Genetic algorithms</subject><subject>genetics-based machine learning</subject><subject>Harnesses</subject><subject>Learning</subject><subject>Learning and adaptive systems</subject><subject>learning classifier systems</subject><subject>Lifting equipment</subject><subject>Machine learning</subject><subject>Performance degradation</subject><subject>Personalizing</subject><subject>Players</subject><subject>Signal mapping</subject><subject>Software</subject><subject>user context</subject><subject>XCS</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE9LAzEQxYMoWKsfQLwsiOhl60w2fzZHWasWCgpW6S3EkJWt201Ntgf99Ka09OBlZmB-78F7hJwjjBBB3c7G79WIAqg0KDIhDsgAFcMcgIrDdEOpcinL-TE5iXEBgIyjGpCbefWa1T5kLy5E35m2-W26z-zexa_er7JJ17tQG-viKTmqTRvd2W4PydvDeFY95dPnx0l1N81twXmfU2mxMK6UDLkUzDiKAqA2VCDFgsMHK9GmB1e0sNaoGoRjgkrDlQUrTTEk11vfVfDfaxd7vWyidW1rOufXUctSUprEZSIv_5ELvw4pQtQIVKawClSicEvZ4GMMrtar0CxN-EmQ3lSnN9XpTXV6V13SXO2cTbSmrYPpbBP3QlpAyZjgibvYco1zbv_mjMpkXPwBYHNzjQ</recordid><startdate>20100801</startdate><enddate>20100801</enddate><creator>Shankar, Anil</creator><creator>Louis, Sushil J</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on evolutionary computation, 2010-08, Vol.14 (4), p.547-560 |
| issn | 1089-778X 1941-0026 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Application software Applied sciences Artificial intelligence Calendars Classifiers Computer science control theory systems Computer systems and distributed systems. User interface Data mining Decision-trees Degradation Evolutionary algorithms Evolutionary computation Exact sciences and technology Field programmable gate arrays Genetic algorithms genetics-based machine learning Harnesses Learning Learning and adaptive systems learning classifier systems Lifting equipment Machine learning Performance degradation Personalizing Players Signal mapping Software user context XCS |
| title | XCS for Personalizing Desktop Interfaces |
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