Graph-based recommendations: from data representation to feature extraction and application

Amit Tiroshi, Tsvi Kuflik, Shlomo Berkovsky, MohamedAli Kaafar

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business systems) and is evaluated using several state-of-the-art machine-learning methods, on different recommendation tasks, and using different evaluation metrics. Overall, the results show an unanimous improvement in the recommendation accuracy across tasks and domains.
Original languageEnglish
Title of host publicationIET - Big Data Recommender Systems
Subtitle of host publicationApplication Paradigms
PublisherInstitution of Engineering and Technology
Number of pages48
ISBN (Electronic)9781785619786, 9781785619779
ISBN (Print)9781785619779
StatePublished - 1 Jan 2019

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2020.


  • Combinatorial mathematics
  • Data handling
  • Data handling techniques
  • Data representation
  • Data structures
  • Evaluation metrics
  • Feature extraction
  • File organisation
  • Graph theory
  • Graph-based feature extraction
  • Graph-based recommendations
  • Information networks
  • Knowledge engineering techniques
  • Latent preference patterns
  • Learning (artificial intelligence)
  • Machine-learning methods
  • Meta data
  • Metadata
  • Recommendation accuracy
  • Recommendation algorithm
  • Recommendation tasks
  • Recommender systems
  • User interfaces
  • User modelling
  • User models
  • Users modeling

ASJC Scopus subject areas

  • General Computer Science


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