Citation recommendation without author supervision

  • Authors:

  • Qi He;Daniel Kifer;Jian Pei;Prasenjit Mitra;C. Lee Giles

  • Affiliations:

  • The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;Simon Fraser University, Burnaby, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA

  • Venue:
  • Proceedings of the fourth ACM international conference on Web search and data mining
  • Year:
  • 2011

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Abstract

Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (without a bibliography) and our system automatically finds locations where citations are needed. We show that naïve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.