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FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation

  • Ankesh AnandEmail author
  • Tanmoy Chakraborty
  • Amitava Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

In this paper, we present \(\mathsf {FairScholar}\), a novel scientific paper recommendation system that aims at balancing both relevance and diversity while searching for research papers in response to keyword queries. Our system performs a vertex reinforced random-walk, a time heterogeneous random-walk on the citation graph of papers in order to factor in diversity while serving recommendations. To incorporate semantically similar items in the search results, it uses a query expansion step that finds similar keywords using community detection. An online demo of our search engine is available at http://www.cnergres.iitkgp.ac.in/FairScholar/.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Indian Institute of TechnologyKharagpurIndia
  2. 2.University of MarylandCollege ParkUSA
  3. 3.Indian Institute of Information Technology Sri CitySri CityIndia

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