Rights statement: This is a research-in-progress paper. Please check with the author for an updated version.
Accepted author manuscript, 322 KB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Using viewing time to infer user preference in recommender systems
AU - Parsons, J
AU - Ralph, P
AU - Gallagher, K
N1 - This is a research-in-progress paper. Please check with the author for an updated version.
PY - 2004
Y1 - 2004
N2 - The need for effective technologies to help Web users locate items (information or products) is increasing as the amount of information on the Web grows. Collaborative filtering is one of the most successful techniques for making recommendations; however, most CF-based systems require explicit user ratings and a large quantity of usage history to function effectively. In addition, such systems typically rely on comparing a user to ‘‘similar’’ users encountered before. We develop and evaluate the idea that viewing time is an indicator of preference for attributes of items, and a recommendation system based on this idea. The system uses only the current user’’s navigational data in conjunction with item property data to make recommendations. We also present empirical evidence that the system makes useful recommendations.
AB - The need for effective technologies to help Web users locate items (information or products) is increasing as the amount of information on the Web grows. Collaborative filtering is one of the most successful techniques for making recommendations; however, most CF-based systems require explicit user ratings and a large quantity of usage history to function effectively. In addition, such systems typically rely on comparing a user to ‘‘similar’’ users encountered before. We develop and evaluate the idea that viewing time is an indicator of preference for attributes of items, and a recommendation system based on this idea. The system uses only the current user’’s navigational data in conjunction with item property data to make recommendations. We also present empirical evidence that the system makes useful recommendations.
M3 - Conference contribution/Paper
BT - AAAI Workshop in Semantic Web Personalization (California) - 2004
PB - unknown
CY - N/A
ER -