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A new neighbourhood-based diffusion algorithm for personalized recommendation

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Abstract

Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms.

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Acknowledgements

The work reported in this paper has been supported by the National Natural Science Foundation of China (Grant number: 62302199), the China Postdoctoral Science Foundation (Grant number: 2023M731368), the Natural Science Foundation of the Jiangsu Higher Education Institutions (Grant number: 22KJB520016), and Jiangsu University Innovative Research Project (KYCX22_3671).

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Authors and Affiliations

  1. Tamale Technical University, P.O.Box ER3, Tamale, Ghana

    Diyawu Mumin

  2. Jiangsu University, Zhenjiang, 212013, China

    Diyawu Mumin, Lei-Lei Shi, Zi-xuan Han & Yan Wu

  3. School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK

    Lu Liu

  4. Ocean College, Jiangsu University of Science and Technology, Jiangsu, China

    Liang Jiang

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  1. Diyawu Mumin
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  2. Lei-Lei Shi
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Contributions

Diyawu Mumin and Lei-Lei Shi wrote the main manuscript text, and Liu Lu helped in the experiments. Zi-xuan Han and Liang Jiang did the data processing and analysis. All authors reviewed the manuscript.

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Correspondence to Lu Liu.

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Mumin, D., Shi, LL., Liu, L. et al. A new neighbourhood-based diffusion algorithm for personalized recommendation. Knowl Inf Syst 66, 5389–5408 (2024). https://doi.org/10.1007/s10115-024-02127-1

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