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Semantic user profiling techniques for personalised multimedia recommendation

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Abstract

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme.

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Notes

  1. http://dbpedia.org/About.

  2. http://www.opencalais.com/.

  3. http://mg4j.dsi.unimi.it/.

  4. http://news.bbc.co.uk/.

  5. http://alias-i.com/lingpipe.

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Acknowledgments

This research was supported by the European Commission under contract FP6-027122-SALERO. It is the view of the authors but not necessarily the view of the community.

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  1. Department of Computing Science, University of Glasgow, Glasgow, Scotland, UK

    Frank Hopfgartner & Joemon M. Jose

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  1. Frank Hopfgartner
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  2. Joemon M. Jose
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Correspondence to Frank Hopfgartner.

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Hopfgartner, F., Jose, J.M. Semantic user profiling techniques for personalised multimedia recommendation. Multimedia Systems 16, 255–274 (2010). https://doi.org/10.1007/s00530-010-0189-6

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