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Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation

  • Tommaso Di NoiaEmail author
  • Iván Cantador
  • Vito Claudio Ostuni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation approaches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.

Keywords

Recommender System Recommendation Method Link Open Data Recommendation List Recommendation Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank all participants for their interest in the challenge, their submissions, and presentations and discussion during the conference. We also thank the program committee members for their valuable reviews of submissions, and Valentina Presutti and Milan Stankovic for their help with the organization of the challenge.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tommaso Di Noia
    • 1
    Email author
  • Iván Cantador
    • 2
  • Vito Claudio Ostuni
    • 1
  1. 1.Department of Electrical and Electronic EngineeringPolitecnico di BariBariItaly
  2. 2.Department of Computer ScienceUniversidad Autónoma de MadridMadridSpain

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