TSSP: Multi-features based reinforcement algorithm to find related papers

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.