We present a framework for adaptive news access, based on machine
learning techniques specifically designed for this task. First, we
focus on the system's general functionality and system architecture.
We then describe the interface and design of two deployed news agents
that are part of the described architecture. While the first agent
provides personalized news through a web-based interface, the second
system is geared towards wireless information devices such as PDAs
(personal digital assistants) and cell phones. Based on implicit
and explicit user feedback, our agents use a machine learning algorithm
to induce individual user models. Motivated by general shortcomings
of other user modeling systems for Information Retrieval applications,
as well as the specific requirements of news classification, we propose
the induction of hybrid user models that consist of separate models
for short-term and long-term interests. Furthermore, we illustrate
how the described algorithm can be used to address an important issue
that has thus far received little attention in the Information Retrieval
community: a user's information need changes as a direct result of
interaction with information. We empirically evaluate the system's
performance based on data collected from regular system users. The
goal of the evaluation is not only to understand the performance
contributions of the algorithm's individual components, but also
to assess the overall utility of the proposed user modeling techniques
from a user perspective. Our results provide empirical evidence for
the utility of the hybrid user model, and suggest that effective
personalization can be achieved without requiring any extra effort
from the user.
%0 Journal Article
%1 billsus00
%A Billsus, Daniel
%A Pazzani, Michael J.
%D 2000
%J User Modeling and User-Adapted Interaction
%K adaptive news user_modelling
%N 2
%P 147--180
%R 10.1023/A:1026501525781
%T User Modeling for Adaptive News Access
%U http://dx.doi.org/10.1023/A:1026501525781
%V 10
%X We present a framework for adaptive news access, based on machine
learning techniques specifically designed for this task. First, we
focus on the system's general functionality and system architecture.
We then describe the interface and design of two deployed news agents
that are part of the described architecture. While the first agent
provides personalized news through a web-based interface, the second
system is geared towards wireless information devices such as PDAs
(personal digital assistants) and cell phones. Based on implicit
and explicit user feedback, our agents use a machine learning algorithm
to induce individual user models. Motivated by general shortcomings
of other user modeling systems for Information Retrieval applications,
as well as the specific requirements of news classification, we propose
the induction of hybrid user models that consist of separate models
for short-term and long-term interests. Furthermore, we illustrate
how the described algorithm can be used to address an important issue
that has thus far received little attention in the Information Retrieval
community: a user's information need changes as a direct result of
interaction with information. We empirically evaluate the system's
performance based on data collected from regular system users. The
goal of the evaluation is not only to understand the performance
contributions of the algorithm's individual components, but also
to assess the overall utility of the proposed user modeling techniques
from a user perspective. Our results provide empirical evidence for
the utility of the hybrid user model, and suggest that effective
personalization can be achieved without requiring any extra effort
from the user.
@article{billsus00,
abstract = {We present a framework for adaptive news access, based on machine
learning techniques specifically designed for this task. First, we
focus on the system's general functionality and system architecture.
We then describe the interface and design of two deployed news agents
that are part of the described architecture. While the first agent
provides personalized news through a web-based interface, the second
system is geared towards wireless information devices such as PDAs
(personal digital assistants) and cell phones. Based on implicit
and explicit user feedback, our agents use a machine learning algorithm
to induce individual user models. Motivated by general shortcomings
of other user modeling systems for Information Retrieval applications,
as well as the specific requirements of news classification, we propose
the induction of hybrid user models that consist of separate models
for short-term and long-term interests. Furthermore, we illustrate
how the described algorithm can be used to address an important issue
that has thus far received little attention in the Information Retrieval
community: a user's information need changes as a direct result of
interaction with information. We empirically evaluate the system's
performance based on data collected from regular system users. The
goal of the evaluation is not only to understand the performance
contributions of the algorithm's individual components, but also
to assess the overall utility of the proposed user modeling techniques
from a user perspective. Our results provide empirical evidence for
the utility of the hybrid user model, and suggest that effective
personalization can be achieved without requiring any extra effort
from the user.},
added-at = {2009-06-22T17:28:38.000+0200},
author = {Billsus, Daniel and Pazzani, Michael J.},
biburl = {https://www.bibsonomy.org/bibtex/27a9dc05ed774449ddd6831d98a2222a6/lefteris8},
citeulike-article-id = {2408301},
doi = {10.1023/A:1026501525781},
interhash = {176f415b93c367b2816c0467e377d753},
intrahash = {7a9dc05ed774449ddd6831d98a2222a6},
journal = {User Modeling and User-Adapted Interaction},
keywords = {adaptive news user_modelling},
month = {June},
number = 2,
pages = {147--180},
posted-at = {2009-04-13 21:21:28},
priority = {2},
timestamp = {2009-06-22T17:28:39.000+0200},
title = {User Modeling for Adaptive News Access},
url = {http://dx.doi.org/10.1023/A:1026501525781},
volume = 10,
year = 2000
}