Learning Implicit User Interest Hierarchy for Context in Personalization

Learning Implicit User Interest Hierarchy for Context in Personalization (2003)

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by Hyoung R. Kim , Philip K. Chan

Venue:	In Proc. of International Conference on Intelligent User Interface (IUI
Citations:	32 - 4 self

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Datum	Value	Source
TITLE	Learning Implicit User Interest Hierarchy for Context in Personalization	user correction - Legacy Corrections
AUTHOR NAME	Hyoung R. Kim	user correction - Legacy Corrections
AUTHOR AFFIL	; Department of Computer Sciences; Florida Institute of Technology; Categories and Subject Descriptors; General Terms	user correction - Legacy Corrections
AUTHOR ADDR	; Melbourne, FL 32901, USA	user correction - Legacy Corrections
AUTHOR NAME	Philip K. Chan	user correction - Legacy Corrections
ABSTRACT	To provide a more robust context for personalization, we desire to extract a continuum of general (long-term) to specific (short-term) interests of a user. Our proposed approach is to learn a user interest hierarchy (UIH) from a set of web pages visited by a user. We devise a divisive hierarchical clustering (DHC) algorithm to group words (topics) into a hierarchy where more general interests are represented by a larger set of words. Each web page can then be assigned to nodes in the hierarchy for further processing in learning and predicting interests. This approach is analogous to building a subject taxonomy for a library catalog system and assigning books to the taxonomy. Our approach does not need user involvement and learns the UIH "implicitly." Furthermore, it allows the original objects, web pages, to be assigned to multiple topics (nodes in the hierarchy). In this paper, we focus on learning the UIH from a set of visited pages. We propose a few similarity functions and dynamic threshold-funding methods, and evaluate the resulting hierarchies according to their meaningfulhess and shape.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	In Proc. of International Conference on Intelligent User Interface (IUI	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	101--108	INFERENCE
CITATIONS	15 found	ParsCit 1.0