An Analysis of Citation Recommender Systems: Beyond the Obvious
An Analysis of Citation Recommender Systems: Beyond the Obvious
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DOI：
10.1145/3110025.3110150
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Haofeng Jia;Erik Saule
中科院分区：
文献类型：
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作者：
Haofeng Jia;Erik Saule
随着科学的进步，学术界发表了数以百万计的研究论文。研究人员在撰写论文时会花费时间和精力搜索相关的手稿，或者只是为了跟上当前的研究。在本文中，我们通过扩展一组已知相关的参考文献来考虑引文推荐问题。我们的分析表明，投影图的被引子图的被引度服从幂分布。现有的流行方法只擅长发现长尾论文，即那些与其他论文高度相关的文章。换句话说，大多数被引用的论文在投影图中是松散联系的，但用现有的方法是找不到的。为了解决这一问题，我们建议结合作者、地点和关键词信息来解释这些松散联系的论文背后的引文行为。结果表明，不同的方法找到的被引论文具有很大的不同性质。对于一个实际的引文推荐系统，我们建议采用不同算法的多个推荐列表可以满足不同的用户。
As science advances, the academic community has published millions of research papers. Researchers devote time and effort to search relevant manuscripts when writing a paper or simply to keep up with current research. In this paper, we consider the problem of citation recommendation by extending a set of known-to-be-relevant references. Our analysis shows the degrees of cited papers in the subgraph induced by the citations of a paper, called projection graph, follow a power law distribution. Existing popular methods are only good at finding the long tail papers, the ones that are highly connected to others. In other words, the majority of cited papers are loosely connected in the projection graph but they are not going to be found by existing methods. To address this problem, we propose to combine author, venue and keyword information to interpret the citation behavior behind those loosely connected papers. Results show that different methods are finding cited papers with widely different properties. We suggest multiple recommended lists by different algorithms could satisfy various users for a real citation recommendation system.