Filter-enhanced MLP is All You Need for Sequential Recommendation

TL;DR

This paper introduces FMLP-Rec, an all-MLP sequential recommendation model with learnable filters that effectively reduce noise in user behavior data, outperforming complex models like Transformers in accuracy and efficiency.

Contribution

It proposes a novel all-MLP architecture with adaptive learnable filters for noise reduction, achieving superior performance in sequential recommendation tasks.

Findings

Filtering algorithms significantly improve recommendation models.

FMLP-Rec outperforms Transformer-based models on multiple datasets.

The model has lower time complexity due to its all-MLP design.

Abstract

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}}.