The invention relates to the technical field of sequence recommendation, discloses a
frequency domain collaborative enhancement
side information fusion sequence
recommendation model, and aims to solve the problem that
high frequency or periodic
noise on a
frequency domain is difficult to relieve due to the fact that
side information is fused on a
time domain by an existing method in a current sequence recommendation task. The method comprises the following steps: performing adaptive filtering on a commodity sequence in a
frequency domain to capture deep interests and preferences of a user, and adopting an adaptive threshold to reduce
noise influence; the commodity and
side information sequence is converted to a frequency domain for
feature fusion through
Fourier transform, and important features can be highlighted and irrelevant
noise can be suppressed through the sparse feature of a
frequency spectrum; besides, the model introduces frequency
domain prediction loss to further optimize the prediction precision of the model, in contrast experiments on four public data sets, compared with an optimal
base line, the average Recall (at) 10 and Recall (at) 20 of the method are improved by 4.35% and 4.57% respectively, the average NDCG (at) 10 and NDCG (at) 20 are improved by 2.45% and 3.02% respectively, the optimal performance expression is achieved, and the effectiveness of the method provided by the invention is proved.