A case for interaction: a study of interactive information retrieval behavior and effectiveness
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Supporting interactive information retrieval through relevance feedback
Conference Companion on Human Factors in Computing Systems
Broadcast news navigation using story segmentation
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Some simple effective approximations to the 2–Poisson model for probabilistic weighted retrieval
Readings in information retrieval
Building user and expert models by long-term observation of application usage
UM '99 Proceedings of the seventh international conference on User modeling
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Communications of the ACM
Machine learning of event segmentation for news on demand
Communications of the ACM
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Personalized multimedia information access
Communications of the ACM - The Adaptive Web
User Modeling and User-Adapted Interaction
Improving Broadcast News Segmentation Processing
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Taking advantage of contextualized interactions while users watch TV
Multimedia Tools and Applications
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Broadcast news sources and newspapers provide society with the vast majority of real-time information. Unfortunately, cost efficiencies and real-time pressures demand that producers, editors, and writers select and organize content for stereotypical audiences. In this article we illustrate how content understanding, user modeling, and tailored presentation generation promise personalcasts on demand. Specifically, we report on the design and implementation of a personalized version of a broadcast news understanding system, MITRE's Broadcast News Navigator (BNN), that tracks and infers user content interests and media preferences. We report on the incorporation of Local Context Analysis to both expand the user's original query to the most related terms in the corpus, as well as to allow the user to provide interactive feedback to enhance the relevance of selected newsstories. We describe an empirical study of the search for stories on ten topics from a video corpus. By personalizing both the selection of stories and the form in which they are delivered, we provide users with tailored broadcast news. This individual news personalization provides more fine-grained content tailoring than current personalized television program level recommenders and does not rely on externally provided program metadata.