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      Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

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      Complexity
      Hindawi Limited

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          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.

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          Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

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            A Novel Learning Rate Function and Its Application on the SVD++ Recommendation Algorithm

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              An improved online book recommender system using collaborative filtering algorithm

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                Author and article information

                Contributors
                Journal
                Complexity
                Complexity
                Hindawi Limited
                1099-0526
                1076-2787
                April 5 2021
                April 5 2021
                : 2021
                : 1-11
                Affiliations
                [1 ]College of Horticulture and Gardening, Yangtze University, Jingzhou, Hubei 434025, China
                Article
                10.1155/2021/5555226
                61c3021a-0a62-4d3f-8ce5-9c53a4184e77
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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