Recommender system can provide users with items that meet their potential needs in mass information. Its development provides new ideas and supporting technologies for applications in online education scenarios. The previous recommendation methods usually only consider the enhancement of the item side, but ignore the importance of the user characteristics to the recommendation, and are not suitable for the online education scenario. To address this problem, we take knowledge graph as the auxiliary information source of collaborative filtering and propose an end-to-end framework using knowledge graph to enrich the semantics of the item representation. In particular, faced with the thorny problem of cold start, the framework makes use of the static features of users to personalize the modeling of new users. Experimenting with two public datasets and an industrial dataset, we demonstrate that the framework has significant performance improvements over the baseline and can maintain satisfactory performance with sparse user–item interactions.

Shuang Yang

Xuesong Cai

Information Systems

信息系统是支持数据密集型应用程序的软件和硬件系统。 《信息系统》杂志发表有关信息系统语言、数据模型、流程模型、算法、软件和硬件的设计和实施的文章。主题领域包括主要国际数据库会议（例如 ACM SIGMOD/PODS）中提出的数据管理问题、VLDB、ICDE 和 ICDT/EDBT）以及数据挖掘/机器学习、与结构化数据协调的信息检索、互联网和云数据管理、业务流程管理、网络语义、视觉和音频信息等领域的数据相关问题系统、科学计算和数据科学。与大规模并行数据管理、实践中的容错以及数据密集型系统的专用硬件有关的实施论文也受到欢迎。也欢迎来自城市信息学、社会和自然科学、物联网等应用领域的手稿。所有论文都应突出数据管理问题的创新解决方案，例如新的数据模型、性能增强，并展示这些创新如何有助于实现应用程序的目标。

Information systems are software and hardware systems that support data-intensive applications. The journal "Lecture Notes in Business Information Processing" publishes articles related to the design and implementation of information system languages, data models, process models, algorithms, software, and hardware. The subject areas include data management issues presented at major international database conferences (such as ACM SIGMOD/PODS), VLDB, ICDE, and ICDT/EDBT, as well as data-related issues in fields like data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, network semantics, visual and audio information systems, scientific computing, and data science. Implementation papers related to large-scale parallel data management, fault tolerance in practice, and dedicated hardware for data-intensive systems are also welcome. Manuscripts from application areas such as urban informatics, social sciences, natural sciences, and the Internet of Things are encouraged. All papers should highlight innovative solutions to data management problems, such as new data models and performance enhancements, and demonstrate how these innovations contribute to achieving the goals of applications.

信息系统是支持数据密集型应用程序的软件和硬件系统。《Lecture Notes in Business Information Processing》期刊发表有关信息系统语言、数据模型、流程模型、算法、软件和硬件设计与实施的文章。主题领域包括主要国际数据库会议（如ACM SIGMOD/PODS）中提出的数据管理问题、VLDB、ICDE和ICDT/EDBT，以及数据挖掘/机器学习、与结构化数据协调的信息检索、互联网和云数据管理、业务流程管理、网络语义、视觉和音频信息等领域的数据相关问题。与大规模并行数据管理、实践中的容错以及数据密集型系统的专用硬件相关的实施论文也受到欢迎。此外，来自城市信息学、社会科学、自然科学和物联网等应用领域的手稿也被鼓励提交。所有论文应突出数据管理问题的创新解决方案，例如新的数据模型和性能增强，并展示这些创新如何有助于实现应用程序的目标。

Bilateral knowledge graph enhanced online course recommendation

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2