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.
