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

• URL: http://arxiv.org/abs/2601.19142v1
• Date: Tue, 27 Jan 2026 03:14:20 GMT
• Title: Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
• Authors: Zhicheng Zhang, Zhaocheng Du, Jieming Zhu, Jiwei Tang, Fengyuan Lu, Wang Jiaheng, Song-Li Wu, Qianhui Zhu, Jingyu Li, Hai-Tao Zheng, Zhenhua Dong,
• Abstract summary: LAIN is a plug-and-play framework that incorporates sequence length as a conditioning signal to balance long- and short-sequence modeling.<n>Our work offers a general, efficient, and deployable solution to mitigate length-induced bias in sequential recommendation.
• Score: 50.094751096858204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 paradoxically degrades performance for short-sequence users due to attention polarization and length imbalance in training data. To address this, we propose LAIN(Length-Adaptive Interest Network), a plug-and-play framework that explicitly incorporates sequence length as a conditioning signal to balance long- and short-sequence modeling. LAIN consists of three lightweight components: a Spectral Length Encoder that maps length into continuous representations, Length-Conditioned Prompting that injects global contextual cues into both long- and short-term behavior branches, and Length-Modulated Attention that adaptively adjusts attention sharpness based on sequence length. Extensive experiments on three real-world benchmarks across five strong CTR backbones show that LAIN consistently improves overall performance, achieving up to 1.15% AUC gain and 2.25% log loss reduction. Notably, our method significantly improves accuracy for short-sequence users without sacrificing longsequence effectiveness. Our work offers a general, efficient, and deployable solution to mitigate length-induced bias in sequential recommendation.

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