# END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

Source: [END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation](<https://www.sota2.com/research/paper/68fa72b31e209131cea5804b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:32:05.519Z

Authors: Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, Enhong Chen

Published: 2024-03-26T11:28:31.000Z

## Abstract

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to compare the valid information in the data with the noisy signal, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [CVR Prediction on Taobao Dataset (test)](<https://www.sota2.com/research/sota/cvr-prediction-on-taobao-dataset-test.md>): CVR prediction; Taobao Dataset (test); AUC: 0.7405.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.17603>)

[PDF](<https://arxiv.org/pdf/2403.17603.pdf>)