Graph-based recommendation integrating rating history and domain knowledge: Application to on-site guidance of museum visitors

Einat Minkov, Keren Kahanov, Tsvi Kuflik

Research output: Contribution to journalArticlepeer-review


Visitors to museums and other cultural heritage sites encounter a wealth of exhibits in a variety of subject areas, but can explore only a small number of them. Moreover, there typically exists rich complementary information that can be delivered to the visitor about exhibits of interest, but only a fraction of this information can be consumed during the limited time of the visit. Recommender systems may help visitors to cope with this information overload. Ideally, the recommender system of choice should model user preferences, as well as background knowledge about the museum's environment, considering aspects of physical and thematic relevancy. We propose a personalized graph-based recommender framework, representing rating history and background multi-facet information jointly as a relational graph. A random walk measure is applied to rank available complementary multimedia presentations by their relevancy to a visitor's profile, integrating the various dimensions. We report the results of experiments conducted using authentic data collected at the Hecht museum. An evaluation of multiple graph variants, compared with several popular and state-of-the-art recommendation methods, indicates on advantages of the graph-based approach.

Original languageEnglish
Pages (from-to)1911-1924
Number of pages14
JournalJournal of the Association for Information Science and Technology
Issue number8
StatePublished - 1 Aug 2017

Bibliographical note

Funding Information:
This work was partially supported by ISF grant No. 1108/2014.

Publisher Copyright:
© 2017 ASIS&T

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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