XCS for personalizing desktop interfaces

Authors:
Anil Shankar;Sushil J. Louis
Affiliations:
Evolutionary Computing Systems Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NY;Evolutionary Computing Systems Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NY
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2010

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Abstract

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 xes 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.