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Personalized News Video Recommendation Via Interactive Exploration

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2008

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Fan, Jianping
Luo, Hangzai
Zhou, Aoying

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https://doi.org/10.1007/978-3-540-89646-3_37
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BEBIS, George, ed. and others. Advances in visual computing : 4th international symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008 ; proceedings, Part 2. Berlin: Springer, 2008, pp. 380-389. Lecture notes in computer science. 5359. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-540-89645-6. Available under: doi: 10.1007/978-3-540-89646-3_37

Zusammenfassung

In this paper, we have developed an interactive approach to enable personalized news video recommendation. First, multi-modal information channels (audio, video and closed captions) are seamlessly integrated and synchronized to achieve more reliable news topic detection, and the contextual relationships between the news topics are extracted automatically. Second, topic network and hyperbolic visualization are seamlessly integrated to achieve interactive navigation and exploration of large-scale collections of news videos at the topic level, so that users can have a good global overview of large-scale collections of news videos at the first glance. In such interactive topic network navigation and exploration process, the user’s personal background knowledge can be taken into consideration for obtaining the news topics of interest interactively, building up their mental models of news needs precisely and formulating their searches easily by selecting the visible news topics on the screen directly. Our system can further recommend the relevant web news, the new search directions, and the most relevant news videos according to their importance and representativeness scores. Our experiments on large-scale collections of news videos have provided very positive results.

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Fachgebiet (DDC)
004 Informatik

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Topic network, personalized news video recommendation

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4th International Symposium on Visual Computing, ISVC 2008, 1. Dez. 2008 - 3. Dez. 2008, Las Vegas, NV, USA
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ISO 690FAN, Jianping, Hangzai LUO, Aoying ZHOU, Daniel A. KEIM, 2008. Personalized News Video Recommendation Via Interactive Exploration. 4th International Symposium on Visual Computing, ISVC 2008. Las Vegas, NV, USA, 1. Dez. 2008 - 3. Dez. 2008. In: BEBIS, George, ed. and others. Advances in visual computing : 4th international symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008 ; proceedings, Part 2. Berlin: Springer, 2008, pp. 380-389. Lecture notes in computer science. 5359. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-540-89645-6. Available under: doi: 10.1007/978-3-540-89646-3_37
BibTex
@inproceedings{Fan2008Perso-58768,
  year={2008},
  doi={10.1007/978-3-540-89646-3_37},
  title={Personalized News Video Recommendation Via Interactive Exploration},
  number={5359},
  isbn={978-3-540-89645-6},
  issn={0302-9743},
  publisher={Springer},
  address={Berlin},
  series={Lecture notes in computer science},
  booktitle={Advances in visual computing : 4th international symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008 ; proceedings, Part 2},
  pages={380--389},
  editor={Bebis, George},
  author={Fan, Jianping and Luo, Hangzai and Zhou, Aoying and Keim, Daniel A.}
}
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    <dcterms:abstract xml:lang="eng">In this paper, we have developed an interactive approach to enable personalized news video recommendation. First, multi-modal information channels (audio, video and closed captions) are seamlessly integrated and synchronized to achieve more reliable news topic detection, and the contextual relationships between the news topics are extracted automatically. Second, topic network and hyperbolic visualization are seamlessly integrated to achieve interactive navigation and exploration of large-scale collections of news videos at the topic level, so that users can have a good global overview of large-scale collections of news videos at the first glance. In such interactive topic network navigation and exploration process, the user’s personal background knowledge can be taken into consideration for obtaining the news topics of interest interactively, building up their mental models of news needs precisely and formulating their searches easily by selecting the visible news topics on the screen directly. Our system can further recommend the relevant web news, the new search directions, and the most relevant news videos according to their importance and representativeness scores. Our experiments on large-scale collections of news videos have provided very positive results.</dcterms:abstract>
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