The application of traditional collaborative filtering algorithm on large-scale commercial websites is very mature. However, the data sparsity and extensibility problems that occur in the algorithm affect the recommendation accuracy of the algorithm. In order to solve this problem, a SOM clustering collaborative filtering algorithm based on singular value decomposition is proposed. Firstly, the original sparse matrix is reduced by the singular value decomposition, and the items are evaluated in the low-dimensional space, the prediction results are filled in the original matrix, which alleviates the problem of data sparseness. Then use SOM to cluster the users, which reduces the range of users searching for neighbors and improves the scalability of the algorithm. The experimental results on MovieLens-100k show that the algorithm can effectively improve the accuracy of the recommendation.

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Noulapeu Ngaffo AChoukair Z(2022)A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularizationNeural Computing and Applications10.1007/s00521-021-06831-934:9(6991-7003)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06831-9
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

Index Terms

SOM Clustering Collaborative Filtering Algorithm Based on Singular Value Decomposition
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Non-negative matrix factorization

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ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

April 2019

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ISBN:9781450362580

DOI:10.1145/3325730

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Noulapeu Ngaffo AChoukair Z(2022)A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularizationNeural Computing and Applications10.1007/s00521-021-06831-934:9(6991-7003)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06831-9
Ngaffo AAyeb WChoukair Z(2020)A Bayesian Inference Based Hybrid Recommender SystemIEEE Access10.1109/ACCESS.2020.29988248(101682-101701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2998824

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Combining Singular Value Decomposition and Item-based Recommender in Collaborative Filtering

Collaborative filtering recommendation algorithm integrating time windows and rating predictions

A Rough Set-Based Clustering Collaborative Filtering Algorithm in E-commerce Recommendation System

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