Qianhui Zhu — accepted AI-conference papers

2026

CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation

Jingyu Li, Zhaocheng Du, Qianhui Zhu, kaiyuan Li, Zhicheng Zhang, Song-Li Wu +2

ICLR 2026poster

Sequential recommendation models are widely used in applications, yet they face stringent latency requirements. Mainstream models leverage the Transformer attention mechanism to improve performance, but its computational complexity grows with the sequence length, leading to a latency challenge for…

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2026

Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction

Zhicheng Zhang, Zhaocheng Du, Jieming Zhu, Jiwei Tang, Fengyuan Lu, Wang Jiaheng +5

AAAI 2026technical

User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While longer sequences provide more context, we observe that increasing the maximum input sequence length in existing CTR models

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