Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers

Research Article

Mobile-to-Mobile Video Recommendation

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  • @INPROCEEDINGS{10.1007/978-3-642-40238-8_2,
        author={Padmanabha Seshadri and Mun Chan and Wei Ooi},
        title={Mobile-to-Mobile Video Recommendation},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2013},
        month={9},
        keywords={mobile-to-mobile communication memory based collaborative filtering coverage},
        doi={10.1007/978-3-642-40238-8_2}
    }
    
  • Padmanabha Seshadri
    Mun Chan
    Wei Ooi
    Year: 2013
    Mobile-to-Mobile Video Recommendation
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-642-40238-8_2
Padmanabha Seshadri1,*, Mun Chan1,*, Wei Ooi1,*
  • 1: National University of Singapore
*Contact email: padmanab@comp.nus.edu.sg, chanmc@comp.nus.edu.sg, ooiwt@comp.nus.edu.sg

Abstract

Mobile device users can now easily capture and socially share video clips in a timely manner by uploading them wirelessly to a server. When attending crowded events, however, timely sharing of videos becomes difficult due to choking bandwidth in the network infrastructure, preventing like-minded attendees from easily sharing videos with each other through a server. One solution to alleviate this problem is to use direct device-to-device communication to share videos among nearby attendees. Contact capacity between two devices, however, is limited, and thus a recommendation algorithm is needed to select and transmit only videos of potential interest to an attendee. In this paper, we address the question: which video clip should be transmitted to which user. We proposed an video transmission scheduling algorithm, called , that runs in a distributed manner and aims to improve both the prediction coverage and precision of the recommendation algorithm. At each device, CoFiGel transmits the video that would increase the estimated number of positive user-video ratings the most if this video is transferred to the destination device. We evaluated using real-world traces and show that substantial improvement can be achieved compared to baseline schemes that do not consider rating or contact history.