A Hybrid User Model for News Story Classification Export

@inproceedings{citeulike:6081626, abstract = {We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user\&\#039;s preferences and interests. In this paper we focus on the system\&\#039;s user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user\&\#039;s information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both model components.}, author = {And, Daniel B. and Billsus, Daniel and Pazzani, Michael J.}, booktitle = {In Proceedings of the Seventh International Conference on User Modeling (UM '99}, citeulike-article-id = {6081626}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7376}, keywords = {hybrid\_user\_model, personalized\_news, user\_model}, pages = {99--108}, posted-at = {2009-11-06 22:41:23}, priority = {2}, title = {A Hybrid User Model for News Story Classification}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7376}, year = {1999} }

TY - CONF ID - citeulike:6081626 L3 - citeulike-article-id:6081626 N2 - We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user's preferences and interests. In this paper we focus on the system's user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user's information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both model components. EP - 108 TI - A Hybrid User Model for News Story Classification T2 - In Proceedings of the Seventh International Conference on User Modeling (UM ’99 SP - 99 KW - hybrid_user_model KW - personalized_news KW - user_model AU - And, Daniel AU - Billsus, Daniel AU - Pazzani, Michael PY - 1999/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7376 ER -