TSSP: A Reinforcement Algorithm to Find Related Papers

  • Authors:

  • Shen Huang;Gui-Rong Xue;Ben-Yu Zhang;Zheng Chen;Yong Yu;Wei-Ying Ma

  • Affiliations:

  • Shanghai Jiao-Tong University, China;Shanghai Jiao-Tong University, China;Microsoft Research Asia, China;Microsoft Research Asia, China;Shanghai Jiao-Tong University, China;Microsoft Research Asia, China

  • Venue:
  • WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
  • Year:
  • 2004

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Abstract

Content analysis and citation analysis are two common methods in recommending system. Compared with content analysis, citation analysis can discover more implicitly related papers. However, the citation-based methods may introduce more noise in citation graph and cause topic drift. Some work combine content with citation to improve similarity measurement. The problem is that the two features are not used to reinforce each other to get better result. To solve the problem, we propose a new algorithm, Topic Sensitive Similarity Propagation (TSSP), to effectively integrate content similarity into similarity propagation. TSSP has two parts: citation context based propagation and iterative reinforcement. First, citation contexts provide clues for which papers are topic related to and filter out less irrelevant citations. Second, iteratively integrating content and citation similarity enable them to reinforce each other during the propagation. The experimental results of a user study show TSSP outperforms other algorithms in almost all cases.