Concept drift learning and its application to adaptive information filtering
نویسنده
چکیده
Concept Drift Learning and Its Application to Adaptive Information Filtering. (December 2003) Dwi HendratmoWidyantoro, B.S., Institut Teknologi Bandung; M.S., Texas A&M University Co-Chairs of Advisory Committee: Dr. John Yen Dr. Thomas R. Ioerger Tracking the evolution of user interests is a problem instance of concept drift learning. Keeping track of multiple interest categories is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. The first part of this dissertation presents a Multiple ThreeDescriptor Representation (MTDR) algorithm, a novel algorithm for learning concept drift especially built for tracking the dynamics of multiple target concepts in the information filtering domain. The learning process of the algorithm combines the long-term and short-term interest (concept) models in an attempt to benefit from the strength of both models. The MTDR algorithm improves over existing concept drift learning algorithms in the domain. Being able to track multiple target concepts with a few examples poses an even more important and challenging problem because casual users tend to be reluctant to provide the examples needed, and learning from a few labeled data is
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