Personalized recommendation system of modern science and technology resources based on hybrid filtering

San-shan Zhao; Yong Li

doi:10.1117/12.2673733

22 May 2023 Personalized recommendation system of modern science and technology resources based on hybrid filtering

San-shan Zhao, Yong Li

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400E (2023) https://doi.org/10.1117/12.2673733
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

With the rapid development of the science and technology service industry, the number of modern science and technology resources is increasing. In the current science and technology resource recommendation process, the content-based recommendation method is mainly applied for resource filtering. The resource scoring results are easily affected by data sparsity so that the F-measure value of the personalized recommendation results of the system is low. Therefore, this paper proposes a personalized recommendation system of modern science and technology resources based on the hybrid filtering algorithm. Starting from the user's basic information, browsing time, and other data, the multi-dimensional user characteristics are obtained. A bipartite network containing several users and resource items is constructed, and a user-resource rating matrix is defined. Based on the concept of hybrid filtering, the dynamic weighted calculation is carried out on the predictive scoring results of collaborative filtering and content filtering to obtain a reliable comprehensive scoring prediction result, based on which all modern scientific and technological resources are filtered. The recommended resources are sorted in descending order according to the scores of multiple users. The top resources are selected as the personalized recommendation results. The system test results verify that the F-measure value of the proposed system is 0. 85, which meets the requirements of personalized recommendation of science and technology resources.

Citation Download Citation

San-shan Zhao and Yong Li "Personalized recommendation system of modern science and technology resources based on hybrid filtering", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400E (22 May 2023); https://doi.org/10.1117/12.2673733

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KEYWORDS

Tunable filters

Design and modelling

Matrices

Algorithm development

Data modeling

Feature extraction

Genetic algorithms

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