Decoupled Side Information Fusion for Sequential Recommendation (2204.11046v1)

Abstract: Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-the-art methods build on self-attention networks and focus on exploring various solutions to integrate the item embedding and side information embeddings before the attention layer. However, our analysis shows that the early integration of various types of embeddings limits the expressiveness of attention matrices due to a rank bottleneck and constrains the flexibility of gradients. Also, it involves mixed correlations among the different heterogeneous information resources, which brings extra disturbance to attention calculation. Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation. We theoretically and empirically show that the proposed solution allows higher-rank attention matrices and flexible gradients to enhance the modeling capacity of side information fusion. Also, auxiliary attribute predictors are proposed to further activate the beneficial interaction between side information and item representation learning. Extensive experiments on four real-world datasets demonstrate that our proposed solution stably outperforms state-of-the-art SR models. Further studies show that our proposed solution can be readily incorporated into current attention-based SR models and significantly boost performance. Our source code is available at https://github.com/AIM-SE/DIF-SR.

• The paper introduces DIF-SR, a novel approach that decouples side information fusion from item embeddings to improve gradient flow and attention precision.
• It employs a decoupled attention mechanism and auxiliary attribute predictors to better integrate diverse side data into sequential recommendations.
• Empirical tests on multiple datasets confirm that DIF-SR consistently outperforms traditional models, enhancing both scalability and recommendation accuracy.

Overview of "Decoupled Side Information Fusion for Sequential Recommendation"

The paper "Decoupled Side Information Fusion for Sequential Recommendation" introduces a novel approach to enhance the efficiency of sequential recommendation systems by optimizing the utilization of side information. These systems aim to predict the next item a user will interact with based on their historical behavior, and incorporating side information about items can significantly improve these predictions. Traditionally, this integration of side information is performed early in the modeling process, which might restrict the adaptability and accuracy of the recommendation systems due to limitations in expressiveness and gradient flexibility.

Key Contributions and Methodology

1. Identification of Limitations: The paper critiques existing methods that primarily rely on early integration of side information into item embeddings. This early integration can create a rank bottleneck in attention matrices and obscure the gradient flow necessary for effective parameter updates. It also obscures complex correlations among diverse data types, destabilizing attention calculation.
2. Introduction of DIF-SR:

The authors propose the Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR) framework. Unlike traditional methods, DIF-SR shifts the fusion process from input to the attention layer, thereby improving both the model's expressiveness and training adaptability. The framework incorporates multiple components: - Decoupled Attention Mechanism: This mechanism calculates separate attention matrices for each type of side information before merging them, which circumvents the limitations of integrated embeddings. - Auxiliary Attribute Predictors (AAP): AAPs are introduced in a multi-task learning environment to enhance the interplay between side information and item representations, thereby boosting learning capacity.

1. Theoretical and Empirical Validation: The paper provides rigorous mathematical analysis to validate the superiority of DIF-SR, focusing on its ability to address the rank and gradient issues present in prior solutions. Experiments conducted on four publicly available datasets (Beauty, Sports, Toys, and Yelp) indicate that DIF-SR consistently outperforms state-of-the-art sequential recommendation systems. Moreover, DIF-SR's components can be integrated into existing frameworks to provide significant performance improvements.

Implications and Future Directions

The proposed DIF-SR framework has several implications:

• Enhanced Recommendation Quality: By decoupling side information and delaying its integration until the attention layer, models can provide more accurate recommendations by harnessing the full potential of available side data.
• Scalable Framework: The modular design allows it to be easily incorporated into other attention-based models, making it a valuable component for future recommendation systems.

Future research might focus on:

• Application to More Complex Scenarios: Testing DIF-SR on datasets with richer attributes and more complex sequences could yield valuable insights and additional improvements to the methodology.
• Optimization of Hyperparameters: Further exploration into hyperparameter tuning, especially concerning the balance of the attribute predictors, could optimize performance across various contexts.
• Generalization to Other Domains: Extending the application to domains beyond e-commerce and entertainment, where sequential interactions are prevalent and side information abundant.

In conclusion, the DIF-SR framework presents a significant advancement in the field of sequential recommendation systems, offering a fresh perspective on the integration of side information that promises both theoretical and practical benefits. The insights provided by this paper pave the way for further improvements and applications in the domain of recommendation technologies.