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Improving Incremental Nonnegative Matrix Factorization Method for Recommendations Based on Three-Way Decision Making

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Abstract

Nonnegative matrix factorization is comprehensively used in recommendation systems. In an effort to reduce the recommended cost of newly added samples, incremental nonnegative matrix factorization and its variants have been extensively studied in recommendation systems. However, the recommendation performance is incapable of particular applications in terms of data sparsity and sample diversity. In this paper, we propose a new incremental recommend algorithm by improving incremental nonnegative matrix factorization based on three-way decision, called Three-way Decision Recommendations Based on Incremental Non-negative Matrix Factorization (3WD-INMF), in which the concept of positive, negative, and boundary regions are employed to update the new coming samples’ features. Finally, experiments on six public data sets demonstrate the error induced by 3WD-INMF is decreasing as the addition of new samples and deliver state-of-the-art performance compared with existing recommendation algorithms. The results indicate our method is more reasonable and efficient by leveraging the idea of three-way decision to perform the recommendation decision process.

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Notes

  1. https://www.yelp.com/dataset/challenge

  2. http://jmcauley.ucsd.edu/data/amazon.

  3. https://grouplens.org/datasets/movielens/

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Funding

This work is supported by the State Key Program of National Nature Science Foundation of China (61936001), the National Key R&D Program of China (2019YFB2103000), National Nature Science Foundation of China (61876027), Natural Science Foundation of Chongqing (cstc2019jcyj-cxttX0002), the Science and Technology Research Program of Chongqing Education Commission of China (KJQN201900638).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China

    Xiaoxia Zhang, Lu Chen, Ye Wang & Guoyin Wang

Authors
  1. Xiaoxia Zhang
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  2. Lu Chen
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  3. Ye Wang
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  4. Guoyin Wang
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Correspondence to Xiaoxia Zhang or Guoyin Wang.

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Cite this article

Zhang, X., Chen, L., Wang, Y. et al. Improving Incremental Nonnegative Matrix Factorization Method for Recommendations Based on Three-Way Decision Making. Cogn Comput 14, 1978–1996 (2022). https://doi.org/10.1007/s12559-021-09897-8

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  • DOI: https://doi.org/10.1007/s12559-021-09897-8

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