Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.
Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.
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DOI：
10.1371/journal.pone.0158423
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Kording K
中科院分区：
文献类型：
--
作者：
Achakulvisut T;Acuna DE;Ruangrong T;Kording K
寻找相关的出版物对于那些必须科普呈指数级增长的学术材料的科学家来说非常重要。算法可以帮助完成这项任务，因为它们有助于音乐，电影和产品推荐。然而，我们对这些算法的性能与学术材料知之甚少。在这里，我们开发了一个算法和一个附带的Python库，它实现了一个基于文章内容的推荐系统。设计原则是适应新内容，提供近乎实时的建议，并开源。我们在2015年神经科学学会会议的15K海报上测试了这个库。人类策划的主题用于交叉验证算法中的参数，并产生与人类判断最大程度相关的相似性度量。我们表明，我们的算法显着优于基于关键字的建议。这里介绍的工作承诺使学术材料的探索更快，更准确。
Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.