A unified graph model for personalized query-oriented reference paper recommendation

  • Authors:

  • Fanqi Meng;Dehong Gao;Wenjie Li;Xu Sun;Yuexian Hou

  • Affiliations:

  • Tianjin University, Tianjin , China;The Hong Kong Polytechnic University, Hong Kong, Hong Kong;The Hong Kong Polytechnic University, Hong Kong, Hong Kong;The Hong Kong Polytechnic University, Hong Kong, Hong Kong;Tianjin University, Tianjin, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

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Abstract

With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.