  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.
