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Popular Books and Linked Data: Some Results for the ESWC’14 RecSys Challenge

  • Michael SchuhmacherEmail author
  • Christian Meilicke
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

Within this paper we present our contribution to Task 2 of the ESWC’14 Recommender Systems Challenge. First we describe an unpersonalized baseline approach that uses no linked-data but applies a naive way to compute the overall popularity of the items observed in the training data. Despite being very simple and unpersonalized, we achieve a competitive \(F_1\) measure of 0.5583. Then we describe an algorithm that makes use of several features acquired from DBpedia, like author and type, and self-generated features like abstract-based keywords, for item representation and comparison. Item recommendations are generated by a mixture-model of individual classifiers that have been learned per feature on a user neighborhood cluster in combination with a global classifier learned on all training data. While our Linked-Data-based approach achieves an \(\mathrm{F}_1\) measure of 0.5649, the increase over the popularity baseline remains surprisingly low.

Notes

Acknowledgments

We would like to thank our colleagues Arnab Dutta and Johannes Knopp for their valuable contribution to our systems, as well as Orphee De Clercq and Robert Meusel for their support in understanding the data and technology used.

References

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    Herlocker, J., Konstan, J., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002)CrossRefGoogle Scholar
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    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

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