Citation recommendation without author supervision

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011
Pages755-764
Number of pages10
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event4th ACM International Conference on Web Search and Data Mining, WSDM 2011 - Hong Kong, China
Duration: 9 Feb 201112 Feb 2011

Other

Other4th ACM International Conference on Web Search and Data Mining, WSDM 2011
CountryChina
CityHong Kong
Period9/2/1112/2/11

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Keywords

  • Bibliometrics
  • Context
  • Extraction
  • Recommender systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

Cite this

He, Q., Kifer, D., Pei, J., Mitra, P., & Lee Giles, C. (2011). Citation recommendation without author supervision. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011 (pp. 755-764) https://doi.org/10.1145/1935826.1935926