Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Gao, Ming; Luo, Yonghan; Hu, Xiaonan

doi:10.1155/2022/9054149

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Emon et al [26] used a hybrid approach that combined association rule mining and user-based collaborative filtering to develop a recommendation system that identified the unique interests of different students in learning materials more effectively, resulting in more personalized recommendations. Gao et al [27] proposed a personalized course recommendation model based on a convolutional neural network combined with negative sequence pattern mining. The model models the course-learning sequence as a negative sequence pattern according to the user's course registration, degree of completion, and final grades, and then via a convolutional sequence-embedding model.…”

Section: Course Recommendationmentioning

confidence: 99%

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

Liang,

Zhang,

Guo

et al. 2023

Electronics

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Online learning platforms provide diverse course resources, but this often results in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, existing course recommendation methods face the challenge of accurately recommending the target course that is most relevant to the user because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the actor-critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendations effectively. Furthermore, we propose a policy gradient based on the temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and to improve the accuracy of the recommendation. We evaluate the proposed model using two real datasets, and the experimental results show that the proposed model significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

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Section: Course Recommendationmentioning

confidence: 99%

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

Liang,

Zhang,

Guo

et al. 2023

Electronics

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“…Online course recommendations using Deep Convolutional Neural Networks with Negative Sequence Mining obtained an overall highest precision rate of 39% [19]. Furthermore, the implementation of the Content-Based Filtering method to provide comic selection recommendations resulted in a similarity percentage value of 76.38%, text preprocessing method is being used but the stemming steps for converting the words to their basic form are not explored [20].…”

Section: Introductionmentioning

confidence: 99%

Content-based filtering using cosine similarity algorithm for alternative selection on training programs

Abdurrafi,

Ningsih

2023

J. Soft Comput. Explor.

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The large selection of training programs provided by the Ministry of Manpower of the Republic of Indonesia makes it difficult for prospective trainees to choose a training program that suits their interests and needs. The purpose of this research is to support the selection process so that an appropriate method is needed to recommend the selection of training programs that match the interests and needs of users. One of the selection methods that can be used is the Content-Based Filtering method with similarity measurement using Cosine Similarity. The content-based filtering method is a content-based filtering method, which recommends training programs based on the suitability between the description of the training program and the interests of prospective trainees using the cosine similarity distance measurement. The test results using the Content-Based Filtering method were able to achieve an average precision value of 88%, indicating the ability of the system to provide training program recommendations that are very relevant and in accordance with the interests and needs of the trainees.

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“…This approach effectively alleviates the problem of information overload and improves the efficiency of course selection and the online experience of users (Wang, 2022;Wang et al, 2020). The key to course recommendation lies in accurately positioning each user's learning goals and needs and finding the most suitable course for users (Gao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

BTCBMA Online Education Course Recommendation Algorithm Based on Learners' Learning Quality

Yan-li

2023

International Journal of Information Technologies and Systems Approach

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To address the problems of existing online education curriculum recommendation methods such as low recommendation accuracy, an online education course recommendation algorithm (BTCBMA) considering learner learning quality is proposed. Firstly, the BERT model is combined with the TextCNN model to implement the preliminary extraction of text features. Secondly, the convolution neural networks and BiLSTM networks are used to capture deep features and temporal features in data. Finally, a multi-head attention mechanism is used to extract key information from learner interaction sequences, review texts, and curriculum multiple attributes. Experiments demonstrate that the accuracy, precision, recall, and F1 values of the proposed online course recommendation method in the MOOC dataset are 0.224, 0.241, 0.237, and 0.239, respectively, while in the CN dataset are 0.217, 0.239, 0.227, and 0.233, respectively, and the performance of the proposed method in online education course recommendation is significantly superior to the compared methods. For learners in online learning systems, the proposed method can effectively recommend high-quality courses, which is of great significance for improving the learning quality and learning efficiency of learners.

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Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Cited by 8 publications

References 38 publications

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

Content-based filtering using cosine similarity algorithm for alternative selection on training programs

BTCBMA Online Education Course Recommendation Algorithm Based on Learners' Learning Quality

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