Abstract
The existing personalized recommendation method based on time is merely a time stamp in the traditional personalized recommendation algorithm. This paper proposes an improved personalized recommendation method based on preference-aware and time factor. The forgetting curve is used to track the changed of user’s interests with time, and then a model of personalized recommendation based on dynamic time is built. Finally, the time forgetting factor is introduced in the collaborative filtering to improve the prediction effect of the interest-aware algorithm. The experimental results show that the proposed personalized recommendation method can get more accurate recommendation performance. It also can reduce the computing time and space complexity, and improve the quality of the recommendation of personalized recommendation algorithm.
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Acknowledgements
This work was funded by the National Natural Science Foundation of China (61772282, 61772454, 61373134, 61402234). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX17_0901) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). We declare that we do not have any conflicts of interest to this work.
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Yin, C., Ding, S., Wang, J. (2019). Improved Personalized Recommendation Method Based on Preference-Aware and Time Factor. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_33
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DOI: https://doi.org/10.1007/978-981-13-1328-8_33
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