Course recommendation method based on association relationship analysis
A technology of association relationship and recommendation method, applied in semantic analysis, complex mathematical operations, instruments, etc., can solve data sparsity, cannot obtain multiple relationship information of heterogeneous information networks, and cannot capture high-order neighborhood association information, etc. problems, to achieve the effect of improving accuracy and complete and comprehensive feature information
Pending Publication Date: 2021-07-02
ZHEJIANG UNIV OF TECH
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[0006] However, the traditional recommendation technology faces the problem of data sparsity, cannot obtain various relationship information on the heterogeneous information network, and cannot capture high-order neighborhood association information, and there are problems such as insufficient recommendation accuracy.
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[0091] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.
[0092] In one embodiment, such as figure 1 As shown, an online course recommendation method based on association relationship analysis, including:
[0093] Step 1. Acquire entities according to user application history records of the online course platform, and the entities include courses, users, knowledge concepts, teachers, and videos.
[0094] Entities in this application include but are not limited to courses, users, knowledge concepts, teachers, and videos.
[0095] Step 2. Extract semantic features S for knowledge concept entities k .
[0096] Specifically, the semantic feature ...
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The invention discloses a course recommendation method based on association relationship analysis. Knowledge concepts and an association matrix between a user and other entities, such as the association matrix between the user and other entities are extracted, and an adjacent matrix corresponding to the knowledge concepts and the user is obtained through a meta path; an association graph of entities is constructed for a meta-path adjacent matrix of a user, association characteristics of the user are obtained through an association characteristic extraction module, and a knowledge concept and entity representations of the user under different meta-paths are weighted and merged to obtain a final user entity representation and a final course knowledge concept entity representation; and finally, a final scoring matrix of the user and the knowledge concept is obtained in a matrix decomposition mode, course recommendation is performed on the user according to the scoring matrix, and the recommendation accuracy is improved.
Description
technical field [0001] The invention belongs to the field of educational course recommendation, and in particular relates to an online course recommendation method based on association relationship analysis. Background technique [0002] In recent years, Massive Open Online Courses (MOOCs for short) has gradually become a model of alternative education in the world, and has completely changed the whole world by opening up online resources to provide new educational opportunities. education field. Now there are a lot of MOOC platforms, for example, Coursera, edX and Udacity are the three pioneers, followed by many pioneers from other countries, such as XuetangX in China, Khan Academy in North America, Miriada in Spain, Iversity in German, England FutureLearn in Australia, Open2Study in Australia, Fun in France, Veduca in Brazil, and Schoo in Japan (Qiu et al., 2016). [0003] However, according to statistical analysis of data, the completion rate of students' online courses...
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IPC IPC(8): G06F40/279G06F40/30G06F16/9535G06F17/16G06Q50/20
CPCG06F40/279G06F40/30G06F17/16G06F16/9535G06Q50/205
Inventor 郝鹏翼毛盛宇叶炳龙韩天星白琮
Owner ZHEJIANG UNIV OF TECH