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Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network

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Abstract

With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of them were considered in perspective of a single-layer network, which destroyed the original hierarchy of data and resulted in sparse matrix and poor timeliness. In order to address these problems and improve the accuracy of recommendation, dynamic clustering collaborative filtering recommendation algorithm based on double-layer network is put forward in this paper. Firstly, attribute information of users and items are respectively used to construct the user layer network and the item layer network. Secondly, new hierarchical clustering method is further presented, which separates users into different communities according to dynamic evolutionary clustering. Finally, score prediction and top-N recommendation lists are obtained by similarity between users in each community. Extensive experiments are conducted with three real datasets, and the effectiveness of our algorithm is verified by different metrics.

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Notes

  1. https://grouplens.org/datasets/movielens/100k.

  2. https://archive.ics.uci.edu/ml/datasets/Restaurant%20&%20consumer%20data.

  3. http://www.public.asu.edu/jtang20/datasetcode/truststudy.htm.

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Acknowledgements

This research was supported through National Natural Science Foundation of China (No. 71561020, 61503203, 61702317, 61771297); Fundamental Research Funds for the Central Universities (No. GK201802013).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710119, China

    Jianrui Chen

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Jianrui Chen & Zhihui Wang

  3. College of Science, Inner Mongolia University of Technology, Hohhot, 010051, China

    Bo Wang

  4. College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China

    Zhiping Ouyang

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  1. Jianrui Chen
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Correspondence to Jianrui Chen.

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Chen, J., Wang, B., Ouyang, Z. et al. Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network. Int. J. Mach. Learn. & Cyber. 12, 1097–1113 (2021). https://doi.org/10.1007/s13042-020-01223-2

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  • DOI: https://doi.org/10.1007/s13042-020-01223-2

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