Toward an Adaptive Personalized Web
The main goal of the proposed research is to develop novel techniques
for adaptive and personalized information searching and
navigation guidance on the web. Personalized techniques cater
to specific users and adaptive methods learn from the users'
(potentially changing) behavior. Our effort will lead to more
productive and effective use of the increasing popular web. Rather
than requesting the user to provide his/her interests, we propose
modeling user interests non-intrusively (no direct user involvement)
based on access behavior. We represent user profiles based on access
patterns (Web Access Graphs) and interests (Page
Interestingness Estimators). Instead of using syntactic and
mathematical techniques for page representation, we incorporate
semantic information from WordNet. Skewed training class
distributions (interesting pages are much fewer than uninteresting
ones) and adaptation over time (changing user behavior) are handled
using multiple learned models. To efficiently process large amounts
of data, we investigate parallelized methods for collecting,
collating, and ranking pages. Personalized analyses of results from
search engines yield more precise and relevant pages. We also
identify similar users based on access behaviors and interests
for collaborative filtering.
Publications
- Personalized Ranking of Search Results with Learned User
Interest Hierarchies from Bookmarks,
H. Kim and P. Chan,
WEBKDD Workshop, SIGKDD Conf., 2005.
- Learning Implicit User Interest Hierarchy for Web Personalization, H. Kim, PhD Thesis, Florida Tech, 2005.
- Implicit Indicators for Interesting Web Pages,
H. Kim and P. Chan,
Proc. Intl. Conf. on Web Information Systems and Technologies,
pp. 270-277, 2005.
- Identifying Variable-Length Meaningful Phrases with Correlation Functions
H. Kim & P. Chan,
Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 30-38, 2004.
- Learning Implicit User Interest Hierarchy for Context
in Personalization,
H. Kim and P. Chan,
Proc. Intl. Conf. on Intelligent User Interfaces, p. 101-108, 2003.
- Constructing web user profiles: A non-invasive learning approach,
P. Chan, in Web Usage Analysis and User Profiling,
LNAI 1836, Springer-Verlag, p39-55, 2000.
- A non-invasive learning approach to building
web user profiles,
P. Chan,
KDD-99 Workshop on Web Usage Analysis and User Profiling,
pp. 7-12, 1999.
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Related Work
Last modified: Sat Aug 27 18:36:36 EDT 2005