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TNFRM: A recommendation model based on temporal interest fluctuation with neural networks and fuzzy clustering

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Abstract

With the rapid development of personalized recommendation technology for big data, online data resources have undergone a rapid expansion, presenting features such as complex structure and diverse forms. For the purpose of improving recommendation accuracy, we adopt the idea of divide and conquer to construct a novel hybrid temporal prediction model to adapt to the features of different user interest data fluctuations. Firstly, the users’ data is divided into small and large ranges through fluctuation thresholds. Meanwhile, we introduce a global dynamic factor to adjust the temporal weight decay of the sequence data, and then use the neural network to regress and predict the small range fluctuation series data, at the same time, the large range fluctuation series data is predicted by dividing the fuzzy relationship through the membership degree of fuzzy clustering. Finally, through simulation recommendation experiments, model ablation experiments, and iterative performance experiments by using 8 baseline models and 7 datasets, the results show that the F1 index of the proposed model has increased by an average of 13.21%, the NDCG index has increased by an average of 15.96%, and the convergence iterations are within 600 times. More accurate prediction results can be received through extraction of data feature for different interest fluctuations, which is helpful for optimizing personalized information recommendation services.

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Data Availability

We used public datasets with the URL: http://deepyeti.ucsd.edu/jianmo/amazon/index.html.

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Funding

This work is supported by the Major Program of the National Social Science Foundation of China "Research on the accurate construction of urban and rural community service system driven by big data" (Grant No. 20&ZD154) and Postgraduate Research & Practice Innovation Program of Jiangsu Province "Research on Topic Mining and Knowledge Graph Construction for Time Series Commentary of Online Health Information."(Grant No. KYCX23_0079).

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Authors and Affiliations

  1. School of Information Management, Nanjing University, Nanjing, 210023, Jiangsu, China

    Hao Ding

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  1. Hao Ding
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Hao Ding: Conceptualization of this study, Methodology, Software.

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Correspondence to Hao Ding.

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Ding, H. TNFRM: A recommendation model based on temporal interest fluctuation with neural networks and fuzzy clustering. Appl Intell 53, 25042–25057 (2023). https://doi.org/10.1007/s10489-023-04776-1

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