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  • Open Access

    ARTICLE

    Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

    Zejun Yang1, Denghui Xia1, Jin Liu1, Chao Zheng2, Yanzhen Qu1,3,4, Yadang Chen1, Chengjun Zhang1,2,3,*

    Journal on Internet of Things, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jiot.2021.015401 - 15 July 2021

    Abstract Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify More >

  • Open Access

    ARTICLE

    An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity

    Peng Xu, Jianwei Zhang*

    Journal on Internet of Things, Vol.2, No.1, pp. 13-21, 2020, DOI:10.32604/jiot.2020.09073 - 06 August 2020

    Abstract Nonlocal property is an important feature of natural images, which means that the patch matrix formed by similar image patches is low-rank. Meanwhile, learning good image priors is of great importance for image denoising. In this paper, we combine the image self-similarity with EPLL (Expected patch log likelihood) method, and propose an EPLL denoising model based on internal and external image similarity to improve the preservation of image details. The experiment results show that the validity of our method is proved from two aspects of visual and numerical results. More >

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