Citolytics:一个基于链接的维基百科推荐系统

M. Schwarzer, Corinna Breitinger, M. Schubotz, Norman Meuschke, Bela Gipp
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引用次数: 8

摘要

我们提出了Citolytics -一个新颖的基于链接的维基百科文章推荐系统。在一项初步研究中,与Apache Lucene的MoreLikeThis (MLT)广泛使用的基于文本的方法相比,Citolytics取得了令人鼓舞的结果。在这篇演示论文中,我们描述了我们计划如何通过使用Elasticsearch和Apache Flink为维基百科文章提供推荐,将Citolytics集成到维基百科的基础设施中。此外,我们提出了一个使用维基百科Android应用程序的大规模在线评估设计。使用维基百科数据有几个独特的优势。首先,非常大的用户样本的可用性有助于统计上显著的结果。其次,维基百科架构的开放性允许我们公开源代码和评估数据,从而使其他研究人员受益。如果基于链接的推荐在我们的在线评估中显示出前景,那么在维基百科中部署所呈现的系统将对维基百科的3000多万用户产生深远的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Citolytics: A Link-based Recommender System for Wikipedia
We present Citolytics - a novel link-based recommendation system for Wikipedia articles. In a preliminary study, Citolytics achieved promising results compared to the widely used text-based approach of Apache Lucene's MoreLikeThis (MLT). In this demo paper, we describe how we plan to integrate Citolytics into the Wikipedia infrastructure by using Elasticsearch and Apache Flink to serve recommendations for Wikipedia articles. Additionally, we propose a large-scale online evaluation design using the Wikipedia Android app. Working with Wikipedia data has several unique advantages. First, the availability of a very large user sample contributes to statistically significant results. Second, the openness of Wikipedia's architecture allows making our source code and evaluation data public, thus benefiting other researchers. If link-based recommendations show promise in our online evaluation, a deployment of the presented system within Wikipedia would have a far-reaching impact on Wikipedia's more than 30 million users.
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