TSSP: A Reinforcement Algorithm to Find Related Papers
TSSP: A Reinforcement Algorithm to Find Related Papers
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TSSP：一种查找相关论文的强化算法
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Wei
中科院分区：
文献类型：
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作者：
Shen Huang;Gui;Benyu Zhang;Zheng Chen;Yong Yu;Wei
内容分析和引文分析是推荐系统中常用的两种方法。与内容分析相比，引文分析可以发现更多隐含相关的论文。然而，基于引文的方法可能会在引文图中引入更多的噪声，导致主题漂移。有些工作联合收割机内容与引文相结合，以改善相似性度量。问题是这两个特征没有被用来相互加强以获得更好的结果。为了解决这个问题，我们提出了一个新的算法，主题敏感的相似性传播（TSSP），有效地将内容相似性相似性传播。TSSP包括两个部分：基于引用上下文的传播和迭代强化。首先，引用上下文提供了哪些论文与主题相关的线索，并过滤掉不太相关的引用。第二，迭代地整合内容和引用相似性，使它们能够在传播过程中相互加强。用户研究的实验结果表明，TSSP在几乎所有情况下都优于其他算法。
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.