Abstract
There are a large number of colleges and universities, and the tasks of assigning jobs in the dormitory are large. How to meet the diverse needs of students for the allocation of dormitory is a subject worth studying. In order to meet the individual needs of students, a deep learning model is first constructed, and the collected data is analyzed and transformed in the model. Secondly, the Pearson coefficient is used to determine the similarity between students, and the student recommendation list is constructed. Finally, the experimental data is used. Verification, the proposed model can effectively improve the accuracy of students’ personalized recommendations.
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Acknowledgements
This research was supported by the project of Nature Scientific Foundation of Heilongjiang Province (F2016038).
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Guo, W., Liu, F. (2020). Personalized Distribution Recommendation Model of Dormitory Based on Deep Learning. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_134
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DOI: https://doi.org/10.1007/978-981-15-1468-5_134
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Online ISBN: 978-981-15-1468-5
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