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Diversifying Citation Recommendations

机译：多元化引文建议

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摘要

Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today's literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk-based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed.-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.
机译：文献检索是学术研究的最重要步骤之一。每年仅计算机科学领域就发表了100,000多篇论文，因此进行完整的文献检索成为一项艰巨的任务。现有的一些文献检索方法和工具无法与当今文献的特征相抗衡，并且存在歧义和同音异义。基于引用信息的技术对于所提到的问题更加健壮。因此，我们最近构建了一个称为“顾问”的Web服务，该服务根据研究人员的兴趣向研究人员提供个性化的建议。由于大多数推荐方法可能返回多余的结果，因此有必要使搜索过程的结果多样化，以增加通过自动搜索可以到达的信息量。本文针对基于引文的书目搜索中的结果多样化问题，假设引文图本身是唯一可用的信息，并且不知道类别或意图。这项工作的贡献是三方面的。我们调查了各种基于随机行走的多样化方法，并通过方向感知特性对其进行了增强，以使用户可以阅读旧的，基础的（可能被引用并广为人知）研究论文或最新的（最可能是鲜为人知的）研究论文。接下来，我们提出了一组基于顶点选择和查询细化的新颖算法。一组具有各种评估标准的实验表明，所提出的-RLM算法比现有方法具有更好的性能，并且在实践中适合于实时书目搜索。

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