The dynamic web presentations with a generality model on the news domain

  • Authors:
  • Hyun Woong Shin;Eduard Hovy;Dennis McLeod

  • Affiliations:
  • Samsung Electronics Co., Ltd., Suwon-City, Gyeonggi-Do, Korea;Information Sciences Institute of the University of Southern California, CA;Computer Science Department, Integrated Media Systems Center, University of Southern California, Los Angeles, CA

  • Venue:
  • SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
  • Year:
  • 2008

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Abstract

Over the last decade, Web and multimedia data have grown at a staggering rate. Users of new media now have great expectations of what they can see on the Web. In addition, most information retrieval systems, including Web search engines, use similarity ranking algorithms based on a vector space model to find relevant information in response to a user's request. However, the retrieved information is frequently irrelevant, because most of the current information systems employ index terms or other techniques that are variants of term frequency. This paper proposed a new approach, named "the dynamic multimedia presentations with a Generality Model," to offer a customized multimodal presentation for an intended audience. Moreover, we proposed a new criterion, "generality," that provides an additional basis on which to rank retrieved documents. To support multi-modal presentation, our proposed story model created story structures that can be dynamically instantiated for different user requests from various multi-modal elements. The generality is a level of abstraction to retrieve results based on desired generality appropriate for a user's knowledge and interests. We compared traditional web news search functions and our story model by using usability test. The result shows that our multimedia presentation methodology is significantly better than the current search functions. We also compared our generality quantification algorithm with human judges' weighting of values to show that the developed algorithm is significantly correlated.