Sequence Recommendation Based on Contrast Learning and Fourier Transform

This paper proposes a sequence recommendation algorithm based on self-attention mechanism and Fourier transform, named CSFTRec. By filtering the noise in the original data, this algorithm maximizes the feature capturing ability of the self-attention mechanism on the sequence data. According to the characteristics of contrast learning, a new contrast loss is introduced on the basis of Bayesian personalized ranking for joint training, which can shorten the distance between different similar sequences. Experiments on eight public data sets show that CSFTRec converges faster and improves the recommendation accuracy by 3% to 5%, which indicates that CSFTRec is more suitable for processing sequence data.