Frequency Meets Intent: Enhancing Sequential Recommendation With Spectral Contrastive Learning

Abstract

Sequential recommendation leverages interaction sequences to model users’ interests and provide personalized recommendations. However, most existing models focus on modeling users’ long-term preferences (low-frequency information) and struggle to perceive sudden changes in users’ interests, limiting the model’s ability to capture users’ short-term preferences (high-frequency information). Furthermore, they rely on the sequential relationship of users’ interaction sequences for modeling, failing to fully capture effective users’ preference information when interaction sequences are sparse, consequently affecting the accurate modeling of users’ latent intents. In this article, we propose an intent-aware spectrally enhanced contrastive learning sequential recommendation model (ISECLRec). Specifically, we design a frequency adaptive reconstruction module to adjust the frequency-domain representation of the sequence to enhance the model’s ability to capture users’ short-term preferences. Meanwhile, we introduce a calibration factor to linearly combine the reconstructed frequency-domain representation with the original sequence representation, comprehensively modeling users’ long-term and short-term preferences. In addition, we construct shared latent intent prototypes and align users’ interaction sequences with their target intents, enabling ISECLRec to accurately capture users’ latent intents. Extensive experimental results on six real-world datasets demonstrate that ISECLRec outperforms advanced baseline models by an average of 4.34% and 3.71% in HR and NDCG, respectively.