Filter-enhanced MLP is All You Need for Sequential Recommendation

By Kun Zhou, Hui Yu, Wayne Xin Zhao, Ji-rong Wen. The Web Conference, 2022

468 citations recorded by the source corpus.

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 (e.g., Band-Stop Filter) with an all-MLP architecture can even outperform competitive Transformer-based models. Motivated by it, we propose 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: https://github.com/RUCAIBox/FMLP-Rec .

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/813c26fb1a2049c7724b82e8aa2f07e98e19edfa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wayne Xin Zhao
2. Ji-rong Wen
3. Yupeng Hou
4. Hongyu Lu
5. Bowen Zheng
6. Yu Chen
7. Yaliang Li
8. Bolin Ding

In Information Systems

1. Wayne Xin Zhao
2. Yupeng Hou
3. Ji-rong Wen
4. Hongyu Lu
5. Bowen Zheng
6. Yu Chen
7. Bolin Ding
8. Yaliang Li

Citations in the corpus

• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023, methodology): “Following previous works [2], [10], [53], we employ the leave-one-out strategy for evaluation.”
• Towards Universal Sequence Representation Learning for Recommender Systems (2022, methodology): “Then various kinds of neural network techniques are proposed to encode interaction sequences, such as Recurrent Neural Network (RNN) [13], Transformer [5, 7, 10, 22], Multilayer Perceptron (MLP) [37] and Graph Neural Network (GNN) [2, 27].”