3 papers
cs.IR2026
SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation
Rui Zhou, Bo Chen, Qinglin Jia +5
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate…
cs.IR2026
DIET: Learning to Distill Dataset Continually for Recommender Systems
Jiaqing Zhang, Hao Wang, Mingjia Yin +6
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, ret…
cs.LG2024
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
Wenjia Xie, Hao Wang, Luankang Zhang +3
Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic…