Explain the mechanism underlying frequency-domain filtering in session-based recommendation

Establish whether frequency-domain filtering improves session-based recommendation because it provides an alternative basis in which entangled, weakly relevant psychological preference components can be more readily separated and down-weighted than in the raw time domain.

Background

The paper identifies an under-explained empirical phenomenon in FMLP-Rec: applying a learnable filter to item embeddings in the frequency domain improves recommendation accuracy, even though recommendation inputs are not physical signals. The authors hypothesize that time-domain behavior represents a mixture of several concurrent psychological preferences, only some of which are relevant to the next clicked item; the remaining components constitute what they call preference noise.

The proposed explanation is explicitly conjectural and interpretive rather than a proven causal mechanism. The paper uses frequency-domain filtering in DTAMLP and reports ablation-level evidence that removing the FFT-based module reduces accuracy, but it does not directly verify that individual frequencies correspond to distinct latent preference components or that filtering improves performance for this reason.