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Extracting viewer interests for automated bookmarking in video-on-demand services

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Abstract

Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challenging to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous studies propose to solve this problem by predicting the jump target locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer’s VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps.

Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmarking the videos. There are two key techniques in our proposed methodology. First, we infer and differentiate viewers’ intentions in VCR jumps by decomposing the interseek times, using an expectation-maximization (EM) algorithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we propose an automated video bookmarking algorithm. The algorithm employs the time-series change detection techniques of CUSUMandMB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences.We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers’ interests within a video can be precisely extracted, and we can position bookmarks on the video’s highlight events accurately. Our proposed video bookmarking methodology does not require any knowledge on video type, contents, and semantics, and can be applied on various types of videos.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, 230027, China

    Yang Zhao & Ye Tian

  2. Polytechnic Institute of New York University, Brooklyn, 11201, USA

    Yong Liu

Authors
  1. Yang Zhao
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  2. Ye Tian
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  3. Yong Liu
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Corresponding author

Correspondence to Ye Tian.

Additional information

Yang Zhao received the BS degree in computer science from University of Science and Technology of China (USTC) in 2009. He is currently a PhD candidate in the Department of Computer Science and Technology in USTC. His research interests include multimedia networks and vehicular ad hoc networks.

Ye Tian is an associate professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). He joined USTC in August 2008. He received his PhD degree from the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK) in December 2007. His research interests include Internet and network measurement, information-centric networks, online social networks, and multimedia networks. He is a member of IEEE, and a senior member of China Computer Federation (CCF). He is currently serving as an associate editor for Frontiers of Computer Science.

Yong Liu is an associate professor at the Electrical and Computer Engineering Department of the Polytechnic Institute of New York University (NYU-Poly). He received his PhD degree from Electrical and Computer Engineering Department at the University of Massachusetts, Amherst, in May 2002. His general research interests lie in modeling, design and analysis of communication networks. His current research directions include Peer-to-Peer systems, overlay networks, network measurement, online social networks, and recommender systems. He is the winner of the IMC Best Paper Award in 2012, INFOCOM Best Paper Award in 2009, and the IEEE Communications Society Best Paper Award in Multimedia Communications in 2008. He is a senior member of IEEE and member of ACM. He is currently serving as an associate editor for IEEE/ACM Transactions on Networking, and Elsevier Computer Networks Journal.

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Zhao, Y., Tian, Y. & Liu, Y. Extracting viewer interests for automated bookmarking in video-on-demand services. Front. Comput. Sci. 9, 415–430 (2015). https://doi.org/10.1007/s11704-014-3490-2

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  • DOI: https://doi.org/10.1007/s11704-014-3490-2

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