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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.19142 |
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| _version_ | 1866910001862279168 |
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| author | Zhang, Zhicheng Du, Zhaocheng Zhu, Jieming Tang, Jiwei Lu, Fengyuan Jiaheng, Wang Wu, Song-Li Zhu, Qianhui Li, Jingyu Zheng, Hai-Tao Dong, Zhenhua |
| author_facet | Zhang, Zhicheng Du, Zhaocheng Zhu, Jieming Tang, Jiwei Lu, Fengyuan Jiaheng, Wang Wu, Song-Li Zhu, Qianhui Li, Jingyu Zheng, Hai-Tao Dong, Zhenhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19142 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction Zhang, Zhicheng Du, Zhaocheng Zhu, Jieming Tang, Jiwei Lu, Fengyuan Jiaheng, Wang Wu, Song-Li Zhu, Qianhui Li, Jingyu Zheng, Hai-Tao Dong, Zhenhua Artificial Intelligence 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. |
| title | Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.19142 |