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-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape
Description
Learning implicit user interest hierarchy for context in personalization
%0 Conference Paper
%1 604064
%A Kim, Hyoung R.
%A Chan, Philip K.
%B IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces
%C New York, NY, USA
%D 2003
%I ACM Press
%K clustering learning user-profile
%P 101--108
%R http://doi.acm.org/10.1145/604045.604064
%T Learning implicit user interest hierarchy for context in personalization
%U http://portal.acm.org/citation.cfm?id=604045.604064
%X 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-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape
%@ 1-58113-586-6
@inproceedings{604064,
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-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape},
added-at = {2007-10-17T14:37:05.000+0200},
address = {New York, NY, USA},
author = {Kim, Hyoung R. and Chan, Philip K.},
biburl = {https://www.bibsonomy.org/bibtex/2e5e639b9c0f20682c657a5480136356a/beate},
booktitle = {IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces},
description = {Learning implicit user interest hierarchy for context in personalization},
doi = {http://doi.acm.org/10.1145/604045.604064},
interhash = {37d713d927af780fb3dd8bf63050b6fb},
intrahash = {e5e639b9c0f20682c657a5480136356a},
isbn = {1-58113-586-6},
keywords = {clustering learning user-profile},
location = {Miami, Florida, USA},
pages = {101--108},
publisher = {ACM Press},
timestamp = {2008-12-09T16:32:51.000+0100},
title = {Learning implicit user interest hierarchy for context in personalization},
url = {http://portal.acm.org/citation.cfm?id=604045.604064},
year = 2003
}