Sequence Recommendation Algorithm Based on Fourier Transform and Recency-Based Sampling
Traditional recommendation algorithms pay more attention to the improvement of the recommendation effect of the model itself.In fact,data quality is more important to the algorithm.But at present,scientific methods for data processing in the field of recommendation algorithms are scattered,and there is no systematic framework.To solve the above problems,this paper proposes a sequence processing framework that can be widely used based on Fourier transform and near end sequence sampling.First,the sequence data is converted into each other in the time domain and frequency domain through Fourier transform,and the useless information in the sequence is filtered according to the characteristics of the sequence data.Secondly,the traditional sliding window sampling method is replaced by the near end sequence sampling method,which accelerates the sample sampling and improves the model's ability to cap-ture the characteristics of the sequence.Through the experiments on five public datasets,when the framework is ap-plied to three different mainstream recommendation algorithms,each model has a 3％-5％ improvement.
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