From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
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DOI：
10.1145/3491102.3517470
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Hyeonsu B Kang;Rafal Kocielnik;Andrew Head;Jiangjiang Yang;Matt Latzke;A. Kittur;Daniel S. Weld;Doug Downey;Jonathan Bragg
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文献类型：
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作者：
Hyeonsu B Kang;Rafal Kocielnik;Andrew Head;Jiangjiang Yang;Matt Latzke;A. Kittur;Daniel S. Weld;Doug Downey;Jonathan Bragg
科学出版物不断增长的步伐需要快速识别相关论文的方法。虽然根据用户兴趣训练的神经网络可以提供帮助，但它们仍然会产生长而单调的建议论文列表。为了改善发现体验，我们引入了多种新方法，用于使用文本相关性消息来增强推荐，这些文本相关性消息突出了推荐论文与用户的出版物和交互历史之间的知识图连接。我们探讨协会介导的作者实体和那些单独使用引用。在一项大规模的真实世界的研究中，我们展示了我们的方法如何显着增加作者的参与度以及未来的参与度，而不会对高引用作者产生偏见。为了扩大消息覆盖的用户较少的出版物或互动的历史，我们开发了一种新的方法，突出与代理作者感兴趣的用户和评估它在一个受控的实验室研究。最后，我们综合设计的影响，为未来的基于图形的消息。
The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user’s publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement—and future engagement when mediated by authors—without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.