Relational social recommendation: Application to the academic domain

Saeed Amal, Chun Hua Tsai, Peter Brusilovsky, Tsvi Kuflik, Einat Minkov

Research output: Contribution to journalArticlepeer-review


This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR.

Original languageEnglish
Pages (from-to)182-195
Number of pages14
JournalExpert Systems with Applications
StatePublished - 15 Jun 2019

Bibliographical note

Publisher Copyright:
© 2019


  • Graph-based recommendation
  • Information extraction
  • Recommendation explainability
  • Social recommendation

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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