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.
This is a preview of subscription content, access via your institution.
Buying options
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.
References
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a Web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Ampazis, N., Emmanouilidis, T.: Exploring semantic features for producing top-N recommendation lists from binary user feedback. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 157–162. Springer, Heidelberg (2014)
Basile, P., Musto, C., De Gemmis, M., Lops, P., Narducci, F., Semeraro, G.: Aggregation strategies for linked open data-enabled recommender systems. In: Presutti, V., et al. (eds.) European Semantic Web Conference (Satellite Events) (2014), CCIS, vol. 475, Springer, Heidelberg (2014)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)
Burke, R.D.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 1–8 (2012)
Heitmann, B., Hayes, C.: SemStim at the LOD-RecSys 2014 Challenge. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 170–175. Springer, Heidelberg (2014)
Kunaver, M., Pozrl, T., Dobravec, S., Kosir, A., Droftina, U.: Increasing top 20 diversity through recommendation post-processing. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 188–192. Springer, Heidelberg (2014)
Maccatrozzo, V., Ceolin, D., Aroyo, L., Groth, P.: A semantic pattern-based recommender. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 182–187. Springer, Heidelberg (2014)
Moreno, A., Ariza-Porras, C., Lago, P., Jiménez-Guarín, C.L., Castro, H., Riveill, M.: Hybrid model rating prediction with linked open data for recommender systems. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 193–198. Springer, Heidelberg (2014)
Peska, L., Vojtas, P.: Hybrid recommending exploiting multiple DBpedia language editions. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 144–149. Springer, Heidelberg (2014)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2011)
Ristoski, P., Mencía, E.L., Paulheim, H.: A hybrid multi-strategy recommender system using linked open data. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 150–156. Springer, Heidelberg (2014)
Schuhmacher, M., Meilicke, C.: Popular books and linked data: some results for the ESWC’14 RecSys challenge. In: Presutti, V., et al. (eds.) SemWebEval 2014, CCIS, vol. 475, pp. 176–181. Springer, Heidelberg (2014)
Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32 (2005)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Di Noia, T., Cantador, I., Ostuni, V.C. (2014). Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation. In: , 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
Download citation
DOI: https://doi.org/10.1007/978-3-319-12024-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12023-2
Online ISBN: 978-3-319-12024-9
eBook Packages: Computer ScienceComputer Science (R0)