  Recently, deep neural networks such as RNN, CNN and Transformer have been
applied in the task of sequential recommendation, which aims to capture the
dynamic preference characteristics from logged user behavior data for accurate
recommendation. However, in online platforms, logged user behavior data is
inevitable to contain noise, and deep recommendation models are easy to overfit
on these logged data. To tackle this problem, we borrow the idea of filtering
algorithms from signal processing that attenuates the noise in the frequency
domain. In our empirical experiments, we find that filtering algorithms can
substantially improve representative sequential recommendation models, and
integrating simple filtering algorithms (eg Band-Stop Filter) with an all-MLP
architecture can even outperform competitive Transformer-based models.
Motivated by it, we propose \textbf{FMLP-Rec}, an all-MLP model with learnable
filters for sequential recommendation task. The all-MLP architecture endows our
model with lower time complexity, and the learnable filters can adaptively
attenuate the noise information in the frequency domain. Extensive experiments
conducted on eight real-world datasets demonstrate the superiority of our
proposed method over competitive RNN, CNN, GNN and Transformer-based methods.
Our code and data are publicly available at the link:
\textcolor{blue}{\url{https://github.com/RUCAIBox/FMLP-Rec}}.
