MIFARec: Mixed-Interest-based Fourier Analysis Network for Time-Specific Recommendation

Abstract

Compared with sequential recommendation, which leverages users’ historical interaction sequences to capture interest characteristics, time-specific recommendation additionally infers user preferences by analyzing historical temporal information and predicts next-item interactions under specified target timestamps. Existing approaches, however, remain limited in effectively representing complex interest dynamics and their temporal evolution. To address the limitations, we propose MIFARec, a Fourier analysis network with mixed-interest representation for the time-specific recommendation task. MIFARec constructs mixed-interest representations within a session by aggregating item embeddings, positional information, and time-interval information. By incorporating global item associations, this approach provides a proxy for longer-term preference patterns and alleviates the limitations of conventional multi-interest frameworks with a fixed number of interests. Such frameworks cannot adequately capture the diverse and hierarchically intertwined nature of user preferences. A fixed number of interests may either fail to represent diverse preferences or introduce redundant interest clusters that weaken recommendation signals. Meanwhile, MIFARec uniquely captures temporal influence on mixed interests by leveraging Fourier analysis. It treats the temporal influence on users as periodic temporal modulation of their mixed interests through a Fourier-feature expansion. Extensive experiments on five public datasets illustrate MIFARec’s superior top-K recommendation performance and computational efficiency compared with state-of-the-art baselines.