DynFreqRec: A frequency-aware training–inference synergistic enhancement framework for sequential recommendation

ADFARec: advanced dynamic modeling with frequency and attention fusion for sequential recommendation

FAESRec: Frequency Adaptive Encoder for Sequential Recommendation

Sequential recommender systems predominantly focus on time-domain modeling, overlooking rich seasonal and periodic structures revealed in the frequency domain. While recent frequency-aware methods leverage Fourier transforms, their reliance on fixed filters introduces two critical limitations: i) inadequate sequence en...

MIDA: Multi-Interest Decoding with Adaptive Aggregation for Session-based Recommendation

Session-based recommendation (SBR) infers user interest from limited interaction sequences, yet current models show a consistent performance drop when longer histories are available. This degradation is associated with decoder designs that overemphasize recent items while underutilizing distant, yet semantically releva...

Information-Aware Long Sequence Compression for Sequential Recommendation

Sequential recommendation (SR) aims to predict a user’s next interaction by modeling temporal dependencies in historical behavior sequences. However, modeling long sequences introduces two challenges: longer histories often include noisy interactions irrelevant to a user’s core interests, and increasing sequence length...

DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain inherently inefficient: standard architectures operate at a fixed rate, allocating comparable computation to every item in a user’s history regardless of its information...

MAPLE: Mamba-Based Adaptive Prototype Learning for Intent Disentanglement in Sequential Recommendation

Sequential recommender systems struggle to simultaneously capture persistent long-term preferences and volatile short-term intentions, while existing dual-stream architectures are computationally inefficient when handling long sequences and suffer from feature entanglement between the two interest streams. To address t...