Abstract
Improving the recommendation accuracy is the ultimate goal of the recommendation system. Based on the recommendation accuracy, a variety of similarity calculation methods are compared and analyzed. Combined with the deep learning mode, a multi-dimensional similarity personalized recommendation model is built in the deep learning mode. Coefficient parameters, repeated training of training and adjustment coefficients in the deep learning training center, determine the personalized recommendation model scheme, through the simulation experiment, the proposed personalized recommendation model can effectively determine the user recommendation list and improve the accuracy of personalized recommendation.
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Acknowledgements
This research was supported by the project of Nature Scientific Foundation of Heilongjiang Province (F2016038).
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Liu, F., Guo, W. (2020). Multi-dimensional Similarity Personalized Recommendation Model in Deep Learning Mode. 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_81
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DOI: https://doi.org/10.1007/978-981-15-1468-5_81
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Online ISBN: 978-981-15-1468-5
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