A Semantic Pattern-Based Recommender

  • Valentina MaccatrozzoEmail author
  • Davide Ceolin
  • Lora Aroyo
  • Paul Groth
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)


This paper presents a novel approach for Linked Data-based recommender systems through the use of semantic patterns - generalized paths in a graph described through the types of the nodes and links involved. We apply this novel approach to the book dataset from the ESWC2014 recommender systems challenge. User profiles are built by aggregating ratings on patterns with respect to each book in provided user training set. Ratings are aggregated by estimating the expected value of a Beta distribution describing the rating given to each individual book. Our approach allows the determination of a rating for a book, even if the book is poorly connected with user profile. It allows for a “prudent” estimation thanks to smoothing. However, if many patterns are available, it considers all the contributions. Additionally, it allows for a lightweight computation of ratings as it exploits the knowledge encoded in the patterns. Our approach achieved a precision of 0.60 and an overall F-measure of about 0.52 on the ESWC2014 challenge.


Lightweight Computation Semantic Patterns Estimation Thanks Recommender Systems Challenge ESWC-2018 Challenge 
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 research was supported by the EU FP7 STREP “ViSTA-TV” project and by the Dutch COMMIT Data2Semantics project.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Valentina Maccatrozzo
    • 1
    Email author
  • Davide Ceolin
    • 1
  • Lora Aroyo
    • 1
  • Paul Groth
    • 1
  1. 1.Department of Computer Science, The Network InstituteVU University AmsterdamAmsterdamThe Netherlands

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