Recommendations in a heterogeneous service environment

Authors:
Christian Überall;Christopher Köhnen;Veselin Rakocevic;Rudolf Jäger;Erich Hoy;Muttukrishnan Rajarajan
Affiliations:
City University London, London, United Kingdom EC1V 0HB and University of Applied Sciences - Technische Hochschule Mittelhessen, Friedberg, Germany 61169;City University London, London, United Kingdom EC1V 0HB and University of Applied Sciences - Technische Hochschule Mittelhessen, Friedberg, Germany 61169;City University London, London, United Kingdom EC1V 0HB;University of Applied Sciences - Technische Hochschule Mittelhessen, Friedberg, Germany 61169;University of Applied Sciences - Technische Hochschule Mittelhessen, Friedberg, Germany 61169;City University London, London, United Kingdom EC1V 0HB
Venue:
Multimedia Tools and Applications
Year:
2013

Citing 7
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Abstract

This paper presents novel algorithms which are able to generate recommendations within a heterogeneous service environment. In this work explicitly set preferences as well as implicitly logged viewing behavior are employed to generate recommendations for Digital Video Broadcast (DVB) content. This paper also discusses the similarity between the DVB genres and YouTube categories. In addition it presents results to show the comparison between well known collaborative filtering methods. The outcome of this comparison study is used to identify the most suitable filtering method to use in the proposed environment. Finally the paper presents a novel Personal Program Guide (PPG), which is used as a tool to visualize the generated recommendations within a heterogeneous service environment. This PPG is also capable of showing the linear DVB content and the non-linear YouTube videos in a single view.