Abstract
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.
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References
Ardissono, L., Portis, F. and Torasso, P. 2001. Architecture of a System for the Generation of Personalization Electronic Programming Guides. Eighth International Conference on User Modeling: Workshop on Personalization in Future TV, Sonthofen, Germany. www. di.unito.it/~;liliana/UM01/ardissono-etal.pdf.
Attar, R. and Fraenkel, A. 1977. Local Feedback in Full-Text Retrieval Systems. Journal of the Association of Computation Machinery, 24(3): 397–417.
Bove, V.M. 1983. Personalcasting: Interactive Local Augmentation of Television Programming. Master’s thesis, MIT, 1983.
Boykin, S. and Merlino, A. 1999. Improving Broadcast News Segmentation Processing. IEEE International Conference on Multimedia and Computing Systems. Florence, Italy. 7–11 June 1999.
Boykin, S. and Merlino, A. 2000. Machine Learning of Event Segmentation for News on Demand. Communications of the ACM, 43(2): 35–41.
Boyle, C. and Encarnacion, A. O. 1994. An Adaptive Hypertext Reading System. User Modeling and User-Adapted Interaction, 4(1): 1–19.
Brusilovsky, P. 1996. Methods and Techniques of Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 6(2-3): 87–129.
Brusilovsky, P. 2001. Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 11: 87–110.
Croft, W. B. and Harper, D. J. 1979. Using Probabilistic Models of Document Retrieval Without Relevance Information. Journal of Documentation, 35(4): 285–295.
Hu, Q. 2003. Audio Hot Spotting. MITRE Sponsored Research Project. <http://www.mitre>. org/news/events/tech03/briefings/intelligent information/hu.pdf
Kaplan, C., Fenwick, J. and Chen. J. 1993. Adaptive Hypertext Navigation based on User Goals and Context. User Modeling and User Adapted Interaction, 3(3): 193–220.
Koenemann, J. 1996. Supporting Interactive Information Retrieval Through Relevance Feedback. CHI 96 Doctoral Consortium. http://www.acm.org/sigchi/chi96/proceedings/ doctoral/Koenemann/Jk2 txt1.htm.
Koenemann, J. and Belkin, N. 1996. A Case For Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness. In: Proceedings of the SIGCHI Conference on Human Factors and Computing Systems. Vancouver, British Columbia, Canada. ACM Press: NY. pp. 205 212. <http://www.acm.org/sigchi/chi96/proceedings/papers/> Koenemann/jk1 txt.htm
Light, M. and Maybury, M. 2002. Personalized Multimedia Information Access: Ask Questions, Get Personalized Answers. Communications of the ACM, 45(5): 54–59. (www.acm.org/ <http://www.acm.org/> cacm/0502/0502toc.html). In: Brusilovsky, P. and Maybury, M. (eds). Special Section on The Adaptive Web.
Linton, F., Joy, D., and Schaefer, H-P. 1999. Building User and Expert Models by Long-Term Observation of Application Usage. In:J. Kay (Ed.), UM99: User Modeling: In: Proceedings of the Seventh International Conference (pp. 129 138). New York: Springer Verlag. [Selected data are accessible from an archive on http://zeus.gmd.de/ml4um/
Maybury, M. Feb. 2000. News on Demand: Introduction. Communications of the ACM, 43(2): 32–34.
Maybury, M., Merlino, A., and Morey, D. 1997. Broadcast News Navigation using Story Segments, ACM International Multimedia Conference, Seattle, WA, November 8-14, 381–391.
Merlino, A. and Maybury, M. 1999. An Empirical Study of the Optimal Presentation of Multimedia Summaries of Broadcast News. In: Mani, I. and Maybury, M. (eds.) Automated Text Summarization, MIT Press.
Merlino, A. 2002. ViTAP News on Demand. Human Language and Technology Conference, San Diego, CA, March 25, 2002.
Robertson, S. E. and Walker, S. 1994. Some Simple Effective Approximations to the 2-Poisson Model for ProbabilisticWeightedRetrieval. In: Proceedings of the 17thAnnualACM-SIGIR Conference on Research and Development in Information Retrieval, 232–241. Reprinted in: K. Sparck Jones and P. Willett (eds) 1997, Readings in Information Retrieval. Morgan Kaufmann, 345 354.
Salton, G. and Buckley, C. 1990. Improving Retrieval Performance by Relevance Feedback. Journal of the American Society for Information Science (JASIS), 41(4): 288–297.
Xu, J. and Croft, W. B. 1996. Query Expansion Using Local and Global Document Analysis. In: Proceedings of the 19th Annual ‘ACM-SIGIR Conference on Research and Development in Information Retrieval, 4–11.
Xu, J. and Croft, W. B. 2000. Improving the Effectiveness of Information Retrieval with Local Context Analysis. ACM Transactions on Information Systems, 18(1): 79–112.
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Maybury, M., Greiff, W., Boykin, S. et al. Personalcasting: Tailored Broadcast News. User Model User-Adap Inter 14, 119–144 (2004). https://doi.org/10.1023/B:USER.0000010142.18921.eb
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DOI: https://doi.org/10.1023/B:USER.0000010142.18921.eb