Title: An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy
Sarabdeep Singh, and Michael Shepherd, and Jack Duffy, and Carolyn Watters (2006)
In Proceedings 69th Annual Meeting of the American Society for Information Science and Technology (ASIST)
Abstract:

A prototype system for the filtering and ranking of news items has been developed and a pilot test has been conducted. The user’s interests are modeled by a user interest hierarchy based on explicit user feedback with adaptive learning after each session. The system learned very quickly, reaching normalized recall values of over 0.9 within three sessions. When the user’s interests “drifted”, the system adapted but the speed with which it adapted seemed dependent on the amount of feedback provided by the user.

See Also: Findory, a news, blog, and podcast tool the offers personalized and adaptive suggestions based on various metrics. Free, easy to use.

See Also: Recommender/Collaborative Filtering Systems
+ MyBestBets Adds More Television Personalization Options
Also included in this post are links to:
+ Pandora (Personalized music and also an illustration of the power of quality metadata)
+ MovieLens (From the University of Minnesota)
Assist in making movie selections. Collaborative filtering from the University of Minnesota
+ Other “personalized” selection tools from ChoiceStream
Including Overstock.com Gift Finder, Yahoo Movie Recommendations, Yahoo Shopping Gift Finder.

This entry was posted on Thursday, January 11th, 2007 at 12:45 am and is filed under Info Management and Retrieval, Information Science, Information Seeking, Search Tools, Software and Web-Based Applications, Technology and Internet. You can follow any responses to this entry through the RSS 2.0 feed. Responses are currently closed, but you can trackback from your own site.