A Learning Automata-Based Singular Value Decomposition and Its Application in Recommendation System

Jing, Yuchun; Jiang, Wen; Su, Guiyang; Zhou, Zhi; Wang, Yifan

doi:10.1007/978-3-319-09339-0_3

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…With the advancement of data size, the information assembled from various views generally contain redundant data and high dimensional property that can increment the computational cost. Several matrix factorizations techniques have gain broad consideration, for example principal component analysis (PCA) [11], Singular Value Decomposition (SVD) [12], non-negative matrix factorization (NMF) [13] etc. Among them, NMF draws much attention of the researchers in multi-view cluster analysis [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Khan

et al. 2022

IEEE Access

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Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multiview data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and L 1 -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.INDEX TERMS Non-negative matrix factorization, multi-view data, manifold structure, nearest neighbor.

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Section: Introductionmentioning

confidence: 99%

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Khan

et al. 2022

IEEE Access

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“…Xu et al proposed a method of integrating the trust relationship of social network friends, effectively alleviating the sparseness of data [8]. In order to reduce the sparsity of the matrix, Jing et al used the singular value decomposition (SVD) method to reduce the dimension of the scoring matrix to lower the sparseness of the matrix [9], but the computation of this method is large. Clustering techniques are used to reduce sparsity.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm based on Spark

Tao¹,

Gan²,

Wen³

2019

IJPE

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With the advent of the era of big data, the problem of information overload has become particularly serious. The recommendation system can provide personalized recommendation services for users by analyzing users' basic information and users' behavior information. How to push information accurately and efficiently has become an urgent issue in the era of big data. Based on the Alternating Least Squares (ALS) collaborative filtering recommendation algorithm, this paper reduces the loss of the invisible factor item attribute information by merging the similarity of the item on the loss function. At the same time, the cold start strategy is introduced into the model to complete the recommendation. The algorithm is implemented on the Spark distributed platform and single node by using the Movie Lens dataset published by the GroupLens Lab. The experiment results show that the proposed recommendation algorithm can preferably alleviate the data sparsity problem compared with the traditional recommendation algorithm. Moreover, the algorithm improves the accuracy of recommendation and the efficiency of calculation.

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Reinforcement learning architecture for cyber–physical–social AI: state-of-the-art and perspectives

Wang

Jin

et al. 2023

Artif Intell Rev

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A Learning Automata-Based Singular Value Decomposition and Its Application in Recommendation System

Cited by 5 publications

References 7 publications

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Collaborative Filtering Recommendation Algorithm based on Spark

Reinforcement learning architecture for cyber–physical–social AI: state-of-the-art and perspectives

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