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Learning to Rank for Educational Search Engines
IEEE Transactions on Learning Technologies ( IF 3.7 ) Pub Date : 2021-04-27 , DOI: 10.1109/tlt.2021.3075196
Arif Usta , Ismail Sengor Altingovde , Rifat Ozcan , Ozgur Ulusoy

In this digital age, there is an abundance of online educational materials in public and proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to build keyword-based search engines over these collections. In modern Web search engines, high-quality rankings are obtained by applying machine learning techniques, known as learning to rank (LTR). In this article, our focus is on constructing machine-learned ranking models to be employed in a search engine in the education domain. Our contributions are threefold. First, we identify and analyze a rich set of features (including click-based and domain-specific ones) to be employed in educational search. LTR models trained on these features outperform various baselines based on ad-hoc retrieval functions and two neural models. As our second contribution, we utilize domain knowledge to build query-dependent ranking models specialized for certain courses or education levels. Our experiments reveal that query-dependent models outperform both the general ranking model and other baselines. Finally, given well-known importance of user clicks in LTR, our third contribution is for handling singleton queries without any click information. To this end, we propose a new strategy to “propagate” click information from the other, similar, queries to the singleton queries. The proposed click propagation approach yields a better ranking performance than the general ranking model and another baseline from the literature. Overall, these findings reveal that both the general and query-dependent ranking models, trained using LTR approaches, yield high effectiveness in educational search, which may ultimately lead to a better learning experience.

中文翻译:

学习为教育搜索引擎排名

在这个数字时代,公共和专有平台上有大量的在线教育材料。为了有效地检索教育资源,有必要在这些集合上构建基于关键字的搜索引擎。在现代 Web 搜索引擎中,高质量的排名是通过应用机器学习技术获得的,称为排名学习 (LTR)。在本文中,我们的重点是构建用于教育领域搜索引擎的机器学习排名模型。我们的贡献是三方面的。首先,我们识别并分析了教育搜索中使用的一组丰富的特征(包括基于点击和特定领域的特征)。在这些特征上训练的 LTR 模型优于基于临时检索功能和两个神经模型的各种基线。作为我们的第二个贡献,我们利用领域知识来构建专门针对某些课程或教育水平的查询相关排名模型。我们的实验表明,查询相关模型的性能优于一般排名模型和其他基线。最后,鉴于 LTR 中用户点击的重要性众所周知,我们的第三个贡献是处理没有任何点击信息的单例查询。为此,我们提出了一种新策略,将来自其他类似查询的点击信息“传播”到单例查询中。所提出的点击传播方法比一般排名模型和文献中的另一个基线产生更好的排名性能。总的来说,这些发现表明,使用 LTR 方法训练的一般和查询相关的排名模型在教育搜索中产生了很高的效率,
更新日期:2021-06-11
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