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.

翻译：在推荐系统中，用户频繁涉及多种类型的行为，如点击、加入购物车和购买。然而，由于行为数据的多样性，用户行为序列在短期内会变得非常长，这给序列推荐模型的效率带来了挑战。同时，部分行为数据也会对用户兴趣建模带来不可避免的噪声。针对上述问题，首先，我们开发了高效行为序列挖掘器（EBM），该挖掘器能在保持低时间复杂度和低参数量的同时，高效捕捉用户行为中的复杂模式。其次，针对不同噪声类型，我们设计了硬去噪和软去噪模块，并充分探索了行为与噪声之间的关系。最后，我们引入了一种对比损失函数及引导训练策略，用于比较数据中的有效信息与噪声信号，并将两个去噪过程无缝集成，以实现噪声信号的高度解耦。在真实数据集上的充分实验证明了我们的方法在处理多行为序列推荐时的有效性和高效性。