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.

中文翻译：

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END4Rec：多行为顺序推荐的高效噪声解耦

在推荐系统中，用户经常进行多种类型的行为，例如点击、添加到购物车和购买。然而，随着行为数据的多样化，用户行为序列在短期内会变得很长，这给序列推荐模型的效率带来了挑战。同时，一些行为数据也会给用户兴趣的建模带来不可避免的噪音。为了解决上述问题，首先，我们开发了高效行为序列挖掘器（EBM），它可以有效捕获用户行为中的复杂模式，同时保持较低的时间复杂度和参数数量。其次，我们针对不同的噪声类型设计了硬去噪模块和软去噪模块，并充分探索行为与噪声之间的关系。最后，我们引入对比损失函数和引导训练策略，将数据中的有效信息与噪声信号进行比较，并将两个去噪过程无缝集成，以实现噪声信号的高度解耦。对现实世界数据集的充分实验证明了我们的方法在处理多行为顺序推荐方面的有效性和效率。