Development of the architecture of a recommendation system for choosing online courses

IF 0.4 Q4 MATHEMATICS, APPLIED
T. A. Shkodina
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引用次数: 0

Abstract

The article provides a rationale for the relevance of developing a recommender system in the field of e-learning. The main approaches to building a recommender system are analyzed: collaborative, content and hybrid filtering. The main objects of the recommender system for choosing online courses are presented: the student, training modules (online courses), elements of knowledge that the user can receive at the end of the training. In algorithmic support, methods for creating recommender systems, such as machine learning, neural networks, genetic algorithms, are considered. Problems in the methods of building recommender systems have been identified: sparseness; cold start; scalability; searching for elements that are most likely to be preferred by the user from a common set of elements. The main problem of recommender systems is to obtain an accurate and high-quality recommendation for the selection of educational objects in accordance with user preferences. It is concluded that it is necessary to build an architecture of a recommender system, including a model of an individual learning trajectory. Filtration of educational objects occurs with the help of a genetic algorithm. The expediency of using a microservice approach to create a web application is determined. The functional tasks of the developed system are highlighted, such as data collection, analysis of user requests, the formation of educational objects using an individual learning trajectory and the issuance of recommendations for choosing online courses. An algorithm for the functioning of the recommender system, a scheme for the operation of the recommender system, as well as information support for the operation of this system have been developed. A general approach to the development of a universal recommender system that can be integrated into the client's service is proposed. The purpose of developing a recommender system for choosing online courses is to provide students with the most appropriate learning objects (sequence of objects) to study in accordance with the characteristics of the student and fragments of knowledge (competencies).
在线课程选择推荐系统体系结构的开发
本文提供了在电子学习领域开发推荐系统的相关理论基础。分析了构建推荐系统的主要方法:协同过滤、内容过滤和混合过滤。提出了在线课程推荐系统的主要对象:学生、培训模块(在线课程)、用户在培训结束时可以获得的知识要素。在算法支持方面,考虑了创建推荐系统的方法,例如机器学习,神经网络,遗传算法。在构建推荐系统的方法中存在的问题已经被确定:稀疏性;冷启动;可伸缩性;从一组公共元素中搜索用户最可能首选的元素。推荐系统的主要问题是如何根据用户的偏好获得准确、高质量的教育对象推荐。结论是有必要建立一个推荐系统的架构,包括一个个人学习轨迹的模型。教育对象的过滤在遗传算法的帮助下进行。使用微服务方法创建web应用程序的便利性是确定的。重点介绍了所开发系统的功能任务,如数据收集、用户请求分析、使用个人学习轨迹形成教育对象以及发布选择在线课程的建议。提出了推荐系统的运行算法、推荐系统的运行方案以及推荐系统运行的信息支持。提出了一种开发通用推荐系统的一般方法,该系统可以集成到客户服务中。开发在线课程选择推荐系统的目的是根据学生的特点和知识片段(能力),为学生提供最合适的学习对象(对象序列)进行学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.70
自引率
0.00%
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0
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