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
[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.
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