Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
This addresses length-induced bias in sequential recommendation systems, offering a deployable solution for improving accuracy across diverse user sequences, though it is incremental as it builds on existing CTR backbones.
The paper tackles the problem of user behavior sequence length heterogeneity in CTR prediction, where longer sequences degrade performance for short-sequence users, and proposes LAIN, a length-adaptive framework that improves overall performance with up to 1.15% AUC gain and 2.25% log loss reduction.
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.