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

Amit Tiroshi*, Tsvi Kuflik, Shlomo Berkovsky, Mohamed Ali (Dali) Kaafar

*Corresponding author for this work

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

Abstract

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 publicationBig data recommender systems
Subtitle of host publicationVolume 2: application paradigms
EditorsOsman Khalid, Samee U. Khan, Albert Y. Zomaya
Place of PublicationLondon
PublisherIET Digital Library
Chapter21
Pages407-454
Number of pages48
ISBN (Electronic)9781785619786
ISBN (Print)9781785619779
DOIs
Publication statusPublished - 2019

Publication series

NameIET Professional Applications of Computing Series
PublisherINST ENGINEERING TECH-IET
Volume35
ISSN (Print)2513-8774

Keywords

  • feature extraction
  • graph theory
  • recommender systems
  • learning (artificial intelligence)
  • data structures
  • data handling
  • user modelling
  • meta data

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