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References (16)

[1]

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment

2025Jiwei Tang, Zhicheng Zhang et al.

[2]

Long-Sequence Recommendation Models Need Decoupled Embeddings

2024Ningya Feng, Junwei Pan et al.

[3]

Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios

2024Jiwei Tang, Jin Xu et al.

[4]

Prompt Tuning as User Inherent Profile Inference Machine

2024Yusheng Lu, Zhaocheng Du et al.

[5]

Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

2024Yongqiang Han, Hao Wang et al.

[6]

Improving Long-Tail Item Recommendation with Graph Augmentation

2023Sichun Luo, Chen Ma et al.

[7]

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

2023Kelong Mao, Jieming Zhu et al.

[8]

BARS: Towards Open Benchmarking for Recommender Systems

2022Jieming Zhu, Kelong Mao et al.

[9]

Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling

2021Bei Yang, Jie Gu et al.

[10]

Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling

2020Jianwen Yin, Chenghao Liu et al.

[11]

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

2020Matthew Tancik, Pratul P. Srinivasan et al.

[12]

Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction

2020Qi Pi, Xiaoqiang Zhu et al.

[13]

MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

2019Hoyeop Lee, Jinbae Im et al.

[14]

Temporal Hierarchical Attention at Category- and Item-Level for Micro-Video Click-Through Prediction

2018Xusong Chen, Dong Liu et al.

[15]

Deep Interest Evolution Network for Click-Through Rate Prediction

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[16]

Deep Interest Network for Click-Through Rate Prediction

2017Guorui Zhou, Cheng-Ning Song et al.

Founder's Pitch

"Enhance CTR prediction by balancing long and short sequence modeling using a Length-Adaptive Interest Network."

AI-Powered Recommender Systems•Score: 7•View PDF ↗

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Why It Matters

In recommendation systems, balancing long and short sequence modeling is crucial as imbalances can degrade system performance, especially in handling diverse user behavior patterns.

Product Angle

Productize LAIN as a modular plugin for existing recommendation systems that can be easily integrated into platforms to improve user engagement metrics.

Disruption

LAIN offers a significant improvement over existing CTR models by addressing sequence length imbalance, potentially setting a new standard in recommendation accuracy.

Product Opportunity

The recommendation systems market is large, with e-commerce, streaming services, and digital advertising among primary consumers; companies in these sectors can benefit from enhanced CTR predictions.

Use Case Idea

Integrate LAIN into e-commerce or content-streaming platforms to enhance their recommendation engines, improving user engagement and personalized content delivery.

Science

LAIN incorporates user sequence length as a signal to improve CTR predictions. It uses spectral length encoding to map sequence length into continuous data, length-conditioned prompting for better context integration, and length-modulated attention to dynamically focus on sequence-specific details.

Method & Eval

Tested on three real-world benchmarks across five strong CTR models, LAIN demonstrated consistent performance improvements, achieving up to 1.15% AUC gain and 2.25% log loss reduction.

Caveats

The primary limitation is the assumption that sequence length can be uniformly integrated across various domains, which may not hold true in all real-world applications.