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.
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Notes
- 1.
Linking Open Data, http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData.
- 2.
Linked Data, http://linkeddata.org.
- 3.
ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, http://challenges.2014.eswc-conferences.org/index.php/RecSys.
- 4.
LibraryThing dataset, http://www.macle.nl/tud/LT.
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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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Di Noia, T., Cantador, I., Ostuni, V.C. (2014). Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_17
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