Show simple item record

dc.contributor.advisorChan, Philip K.
dc.contributor.authorKim, Hyoung-Rae
dc.contributor.authorChan, Philip K.
dc.date.accessioned2016-05-05T14:33:52Z
dc.date.available2016-05-05T14:33:52Z
dc.date.issued2002-10-05
dc.identifier.citationKim, H, Chan, P.K. (2002). Learning implicit user interest hierarchy for context in personalization (CS-2002-14). Melbourne, FL. Florida Institute of Technology.en_US
dc.identifier.otherCS-2002-14
dc.identifier.urihttp://hdl.handle.net/11141/842
dc.description.abstractTo 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-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape.en_US
dc.language.isoen_USen_US
dc.rightsCopyright held by authors.en_US
dc.titleLearning implicit user interest hierarchy for context in personalizationen_US
dc.typeTechnical Reporten_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record