Open AccessResearch on Online Learning Resource Recommendation Method Based on Wide & Deep and Elmo Model
Author(s) -
Jie Liu,
Hong Zhang,
Zihui Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012015
Subject(s) - computer science , artificial intelligence , deep learning , feature (linguistics) , machine learning , resource (disambiguation) , computation , set (abstract data type) , online learning , recommender system , multimedia , algorithm , computer network , philosophy , linguistics , programming language
In order to improve the accuracy of online learning platform recommendation for learner learning resources and to alleviate the cold start problem, this paper proposes an online learning resource recommendation method based on Wide&Deep and Elmo model. The method uses the real learning data set of a university network and educational learning platform, and uses Wide&Deep to deeply explore the deep features of learner characteristics and course content features under the condition of high-dimensional data sparseness, that is, automatic learning combination features, the learner-course feature vector is constructed as the input of the recommendation algorithm. In addition, for the learner’s text feature, it will use the ELMo language model to pre-train the feature vector to improve the recommendation accuracy. CCS Concepts •Computing methodologies➝Cognitive science • Theory of computation➝Online learning algorithms