Abstract
Traditional recommendation algorithms are faced with the problems of low recommendation accuracy and cold start. To solve these problems, a personalized recommendation algorithm model based on user profile and user preference is proposed. It is more effective than the traditional collaborative filtering recommendation algorithm based on users. Firstly, the user model is established based on the user’s historical rating data. When calculating the user similarity, the user profile information is integrated into the user model. Finally, according to the individual profile differences between different users, the user profile parameters are adjusted to further optimize the user model. The experimental results show that the personalized recommendation algorithm based on user profile and user preference model can effectively improve the performance of the recommendation system.
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Acknowledgments
This research is supported by National Key Research and Development Program of China under grant number 2017YFC1405403, and the Philosophical and Social Sciences Research Project of Hubei Education Department under Grant 19Q054, and Green Industry Technology Leding Project (product development category) of Hubei University of Technology under grant number CPYF2017008.
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Zhou, L., Xiong, C., Deng, N., Shen, L. (2020). Personalized Recommendation Algorithm Based on User Preference and User Profile. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_110
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DOI: https://doi.org/10.1007/978-3-030-44038-1_110
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