3 papers
cs.AI2026
CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation
Jingyu Li, Zhaocheng Du, Qianhui Zhu +5
Sequential recommendation models are widely used in applications, yet they face stringent latency requirements. Mainstream models leverage the Transformer attention mechanism to im…
cs.IR2026
MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
Qihang Yu, Kairui Fu, Zhaocheng Du +10
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both i…
cs.AI2026
Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
Zhicheng Zhang, Zhaocheng Du, Jieming Zhu +8
User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While lo…