Graph-based recommendations: Make the most out of social data

Amit Tiroshi, Shlomo Berkovsky, Mohamed Ali Kaafar, David Vallet, Tsvi Kuflik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings
EditorsVania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319087856
StatePublished - 2014
Event22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Netherlands
Duration: 7 Jul 201411 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.


  • Feature extraction
  • Graph-based recommendations
  • Music recommendations
  • Social data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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