[Objective] This paper tries to construct a time series prediction model based on the fluctuation of users’ historical interests, aiming to improve the recommendation results. [Methods] We added time attenuation factor to the ratings by each type of users and linearly fit the data fluctuation with neural network. Then, we chose the optimal parameters to compare the effectiveness of the proposed method. [Results] We conducted five rounds of user simulation tests and found the MAE and RMSE errors of the proposed method were reduced by 47.63% and 44.61%. [Limitations] Analysis of time fluctuation relies on users’ historical data, thus, additional cold-start algorithm is needed to preprocess the data. [Conclusions] The proposed method could effectively analyze and predict the changing of interests in different commodities, and provide more accurate recommendation lists.
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