TY - GEN
T1 - USER
T2 - 7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005
AU - DeLong, Colin
AU - Desikan, Prasanna
AU - Srivastava, Jaideep
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Traditional recommender systems tend to focus on e-commerce applications, recommending products to users from a large catalog of available items. The goal has been to increase sales by tapping into the user's interests by utilizing information from various data sources to make relevant recommendations. Education, government, and policy websites face parallel challenges, except the product is information and their users may not be aware of what is relevant and what isn't. Given a large, knowledge-dense website and a non-expert user searching for information, making relevant recommendations becomes a significant challenge. This paper addresses the problem of providing recommendations to non-experts, helping them understand what they need to know, as opposed to what is popular among other users. The approach is user-sensitive in that it adopts a 'model of learning' whereby the user's context is dynamically interpreted as they browse and then leveraging that information to improve our recommendations.
AB - Traditional recommender systems tend to focus on e-commerce applications, recommending products to users from a large catalog of available items. The goal has been to increase sales by tapping into the user's interests by utilizing information from various data sources to make relevant recommendations. Education, government, and policy websites face parallel challenges, except the product is information and their users may not be aware of what is relevant and what isn't. Given a large, knowledge-dense website and a non-expert user searching for information, making relevant recommendations becomes a significant challenge. This paper addresses the problem of providing recommendations to non-experts, helping them understand what they need to know, as opposed to what is popular among other users. The approach is user-sensitive in that it adopts a 'model of learning' whereby the user's context is dynamically interpreted as they browse and then leveraging that information to improve our recommendations.
UR - http://www.scopus.com/inward/record.url?scp=33751437965&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33751437965&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33751437965
SN - 3540463461
SN - 9783540463467
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 95
BT - Advances in Web Mining and Web Usage Analysis - 7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005, Revised Papers
Y2 - 21 August 2005 through 21 August 2005
ER -