Affiliations: Computer Science and Engineering, Shanghai Jiao-Tong
University, Shanghai 200030, P.R. China | Microsoft Research Asia, 5F, Sigma Center, 49 Zhichun
Road, Beijing 100080, P.R. China
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 and filter out less relevant citations. Second,
iteratively integrating content and citation similarity enable them to
reinforce each other during the propagation. We also expand the basic idea of
TSSP using a weighted content similarity measurement and generalize the whole
algorithm to a multi-features based method. The experimental results of a user
study show the expanded TSSP outperforms other algorithms in most cases.
