Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 45-58    DOI: 10.11925/infotech.2096-3467.2021.0292
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A Personalized Recommendation Model with Time Series Fluctuation of User Interest
Ding Hao1,2,Ai Wenhua1,2,Hu Guangwei1,2(),Li Shuqing3,Suo Wei4
1School of Information Management, Nanjing University, Nanjing 210023, China
2Institute of Government Data Resources,Nanjing University,Nanjing 210023, China
3School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
4School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191,China
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Abstract

[Objective] This paper constructs a prediction model based on hybrid time series to improve the recommendation accuracy. [Methods] First, we constructed a trend prediction model using neural network and fuzzy clustering technique for interest fluctuations at different magnitudes. Then, we utilized neural network to extract and predict the sliding features of small fluctuation series. Finally, we used the membership degree of fuzzy clustering to divide the relationship for large fluctuation series data. [Results] User simulation tests with four groups of experimental data showed that extracted data features for different amplitudes of interest fluctuation yielded more accurate prediction results, which were 19.18% lower than other algorithms’ RMSE and 45.78% higher than other algorithms' Hit-Ratio. [Limitations] The analysis of time fluctuation relies on historical data, therefore, additional cold-start algorithm is needed to preprocess the sparse historical data. [Conclusions] This method could effectively process the fluctuation of interest, and improve the personalized information services.

Received: 24 March 2021      Published: 23 December 2021
 ZTFLH: TP391
Fund:National Social Science Fund of China(20&ZD154);Innovation and Entrepreneurship Big Data and Theoretical Research Project of Nanjing University in 2021(NJU-DI2021004);Key University Science Research Project of Jiangsu Province(19KJA510011)
Corresponding Authors: Hu Guangwei,ORCID：0000-0003-1303-363X     E-mail: hugw@nju.edu.cn