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Related Experiment Video

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A network and visual quality aware N-screen content recommender system using joint matrix factorization.

Farman Ullah1, Ghulam Sarwar1, Sungchang Lee1

  • 1Department of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of Korea.

Thescientificworldjournal
|July 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel N-Screen content recommender system that optimizes visual quality and network bandwidth. It personalizes content delivery by considering user preferences, device capabilities, and network conditions for a better user experience.

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Area of Science:

  • Computer Science
  • Multimedia Systems
  • Human-Computer Interaction

Background:

  • N-Screen services offer versatile multimedia access across diverse devices and networks.
  • Heterogeneous environments introduce Quality of Service (QoS) challenges impacting user experience.
  • Existing recommender systems often overlook network and device capabilities.

Purpose of the Study:

  • To develop a recommender system aware of network and visual quality for N-Screen environments.
  • To enhance user experience by optimizing content delivery based on device and network constraints.
  • To personalize N-Screen content recommendations beyond user preferences alone.

Main Methods:

  • Estimating available bandwidth and visual quality on user's N-Screen devices.
  • Integrating network/device data with user preferences and content genre information.
  • Employing a joint matrix factorization approach combining user ratings, device, and genre similarities.

Main Results:

  • Improved prediction and recommendation accuracy.
  • Enhanced performance in addressing data sparsity and cold-start issues.
  • Demonstrated efficient utilization of network bandwidth and better visual quality.

Conclusions:

  • The proposed system effectively personalizes N-Screen content by considering technical constraints.
  • It offers a significant improvement over existing systems by integrating network-aware and quality-aware features.
  • The approach enhances recommendation accuracy and tackles common challenges in recommender systems.