Now showing 1 - 4 of 4
  • Publication
    Using social ties in group recommendation
    (Intelligent Systems Research Centre, 2011-08-31) ; ;
    The social web is a mass of activity, petabytes of data are generated yearly. The social web has proven to be a great resource for new recommender system techniques and ideas. However it would appear that typically these techniques are not so social, as they only generate recommendations for a user acting alone. In this paper we take the social graph data and preference content (via Facebook) of 94 user study participants and generate social group recommendations for them and their friends. We evaluate how different aggregation policies perform in deciding the final group recommendation. Our findings show that in an offline evaluation an aggregation policy which takes into consideration social weighting outperforms other aggregation policies.
      718
  • Publication
    Introducing social networks and brain computer interaction
    (National University of Ireland, Galway, 2012-06-21) ; ; ;
    It is well known that the brain generates electrical patterns of activity in response to visual stimuli such as faces or any- thing that captures attention in a significant way. Signals of this type can be detected using an EEG (Electroencephalograph) system where we attach electrodes to the scalp and we amplify the detected signals and use a computer to capture them in real time. In this paper we examine the role that automatic sensing of brain activity may have on how users interact with interactive applications like Facebook. This offers a new opportunity for implicit feedback into such systems and in our work we focus on social networking applications. We demonstrate some of these implicit responses with experimental data captured while a user searched Facebook for photos of friends while being connected to an EEG. Finally, we discuss the implications that this kind of automatic implicit feedback may have on future design of such systems.
      3259
  • Publication
    Power to the people : exploring neighbourhood formations in social recommender systems
    The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system field to incorporate this social in- formation into the recommendation process. In this paper we examine the practice of leveraging a user’s social graph in order to generate recommendations. Using various neighbourhood selection strategies, we examine the user satisfaction and the level of perceived trust in the recommendations received.
      1378Scopus© Citations 17
  • Publication
    The social camera : a case-study in contextual image recommendation
    The digital camera revolution has changed the world of photography and now most people have access to, and even regularly carry, a digital camera. Often these cameras have been designed with simplicity in mind: they harness a variety of sophisticated technologies in order to automatically take care of all manner of complex settings (aperture, shutter speed, flash etc.) for point-and-shoot ease, these assistive features are usually incorporated directly into the cameras interface. However, there is little or no support for the end-user when it comes to helping them to compose or frame a scene. To this end we describe a novel recommendation process which uses a variety of intelligent and assistive interfaces to guide the user in taking relevant compositions given their current location and scene context. This application has been implemented on the Android platform and we describe its core user interaction, recommendation technologies and demonstrate its effectiveness in a number of real-world scenarios. Specifically we report on the results of a live-user trial of the technology in a real-world tourist setting.
      1281Scopus© Citations 19