Abstract
In view of the poor recommendation performance of traditional resource collaborative filtering recommendation algorithms, this article proposes a collaborative filtering recommendation model based on deep learning for art and MOOC resources. This model first uses embedding vectors based on the context of metapaths for learning. Embedding vectors based on the context of metapaths aggregate different metapath information and different MOOCs may have different preferences for different metapaths. Secondly, to capture this preference drift, the model introduces an attention mechanism, which can improve the interpretability of the recommendation results. Then, by introducing the Laplacian matrix into the prior distribution of the hidden factor feature matrix, the relational network information is effectively integrated into the model. Finally, compared with the traditional model using the scoring matrix, the model in this article using text word vectors effectively alleviates the impact of data sparsity and greatly improves the accuracy of prediction. After analyzing the experimental results, compared with other algorithms, the resource collaborative filtering recommendation model proposed in this article has achieved better recommendation results, with good stability and scalability.
Highlights
At present, many universities comply with the indicators of the Ministry of Education, using the advantages of the Internet, artificial intelligence, big data analysis technology, putting forward intelligent education
Deep learning can automatically extract deep features. erefore, in order to solve the current problems in the process of collaborative filtering recommendation for MOOC resources, this article designs a collaborative filtering recommendation algorithm for MOOC resources based on deep learning
Wang et al [19] proposed a collaborative filtering recommendation algorithm based on matrix factorization, which has greatly improved the accuracy of recommendation
Summary
Many universities comply with the indicators of the Ministry of Education, using the advantages of the Internet, artificial intelligence, big data analysis technology, putting forward intelligent education. Erefore, the design of a collaborative filtering recommendation algorithm for MOOC resources has very important research significance [4, 5]. With the continuous development of big data technology, MOOC resource recommendation algorithms have emerged. Erefore, in the Internet, collaborative filtering is used to recommend art learning MOOC resources [8, 9]. Due to the increasing volume of resource data, the existing collaborative filtering recommendation algorithm for art learning MOOC resources can only stay on the surface of the data, resulting in higher MAE values. Erefore, in order to solve the current problems in the process of collaborative filtering recommendation for MOOC resources, this article designs a collaborative filtering recommendation algorithm for MOOC resources based on deep learning.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have