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Semantic Movie Recommendations

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Smart Information Systems

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

The overwhelming amount of video and audio content makes it difficult for users to find new high-quality content matching the individual preferences. Recommender systems are built to suggest potentially interesting items by computing the similarity between users and items. The big challenges while creating recommender systems are the sparsity of data (the knowledge about users and items is often limited) and the popularity bias (most recommender algorithms tend to recommend popular items already known to the user). Semantic techniques supporting the graph-based representation of knowledge and the integration of heterogeneous datasets allow us to overcome these problems. The aggregation of knowledge from several different sources enables us to take into account many different aspects while computing recommendations. In addition, semantic recommender systems can provide explanations for suggested items helping the user to understand why an unknown item matches the individual user preferences. In this chapter we discuss the challenges in creating recommender systems and explain semantic approaches for the recommendation domain. We discuss the steps for building a semantic recommender system and present a semantic movie recommender system in detail. The advantages of semantic recommender systems compared to traditional recommender approaches are analyzed.

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Notes

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    http://www.imdb.com/.

  2. 2.

    http://www.freebase.com/.

  3. 3.

    http://dbtropes.org/.

  4. 4.

    http://ir.ii.uam.es/hetrec2011/.

  5. 5.

    http://www.grouplens.org/node/462/.

  6. 6.

    https://grails.org/.

  7. 7.

    http://tomcat.apache.org/.

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Acknowledgments

This research was supported by the Deutsche Forschungsgemeinschaft, DFG, project number AL 561/11-1.

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Authors and Affiliations

  1. Technische Universität Berlin, Berlin, Germany

    Andreas Lommatzsch

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  1. Andreas Lommatzsch
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Correspondence to Andreas Lommatzsch .

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  1. Technische Universität Berlin, Germany

    Frank Hopfgartner

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Lommatzsch, A. (2015). Semantic Movie Recommendations. In: Hopfgartner, F. (eds) Smart Information Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-14178-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-14178-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14177-0

  • Online ISBN: 978-3-319-14178-7

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