Filter-enhanced MLP is All You Need for Sequential Recommendation (2202.13556v1)

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}}.

• The paper introduces FMLP-Rec, showing that an all-MLP architecture with learnable filters can rival complex models.
• It applies frequency domain signal processing to effectively mitigate noise in user interaction data.
• Empirical results across eight datasets demonstrate superior HR@10 and NDCG@10 compared to RNN, CNN, and Transformer models.

A Comprehensive Assessment of Filter-Enhanced MLP Models for Sequential Recommendation

The paper entitled "Filter-enhanced MLP is All You Need for Sequential Recommendation" presents a method that addresses one of the critical challenges in sequential recommendation systems: the noise inherent in user interaction logs. The authors propose a model named FMLP-Rec that cleverly incorporates signal processing techniques into a purely multi-layer perceptron (MLP) framework. This elegant approach promises to enhance the robustness of recommendation systems without resorting to more complex architectures like RNNs, CNNs, or Transformers, which are traditionally susceptible to noise.

Key Contributions of the Study

1. All-MLP Architecture: The paper challenges the necessity of complex architectures in sequential modeling by demonstrating that an all-MLP architecture, augmented with filtering algorithms borrowed from signal processing, can achieve and even surpass state-of-the-art performance observed in Transformer-based models.
2. Incorporation of Learnable Filters: The proposed filters operate in the frequency domain, providing a means to attenuate noise at various frequencies. This filtering strategy shows significant improvements in the quality of the learned user-item interaction sequences.
3. Empirical Evidence: Extensive experiments on eight real-world datasets demonstrate that FMLP-Rec outperforms several advanced models, such as RNN, CNN, GNN, and Transformer-based methods. For example, certain variations of FMLP-Rec demonstrate superior performance in metrics such as HR@10 and NDCG@10 across datasets like Beauty, Sports, and Yelp.
4. Time and Space Complexity: The transition to a purely MLP-based model translates into a more time-efficient process with reduced computational cost, markedly in training phases, showing advantageous time complexity compared to its Transformer counterparts.

Implications of the Research

On a theoretical level, the paper solidifies the potential of frequency domain methods for sequential recommendation. The work also extends the current understanding of the periodicity in user interaction behaviors, which has been traditionally less emphasized in recommendation engine design. Practically, this approach offers a pathway for more efficient yet effective recommendation systems that may better handle user data with inherent noise.

Future Developments

The convergence of signal processing and recommendation opens novel avenues for research. Potential future developments may include, but are not limited to, exploring other types of filters or optimizing filter structures specifically catered to recommendation tasks. There's scope for advancements in adaptive filter learning, perhaps integrating reinforcement learning mechanisms that could dynamically adjust filters in response to changing data patterns.

Additionally, it could be of interest to explore how this framework adapts to more diverse datasets, including those with multilingual or multi-modal interactions, expanding beyond traditional e-commerce or clickstream data to sectors like social media or news recommendations.

Conclusion

This paper successfully demonstrates that a strategically simplified model, like the FMLP-Rec, not only competes effectively with more complex architectures but excels in handling data noise—a prevalent issue in real-world scenarios. It signifies a critical shift in how noise can be perceived and processed within recommendation systems, offering a fresh lens through which the robustness and scalability of these systems can be significantly enhanced. This research exemplifies the profound impact that clever model design and domain knowledge from other fields of paper can have when applied to AI and recommendation systems.