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UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (2110.15114v2)

Published 28 Oct 2021 in cs.IR

Abstract: With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies in its message passing mechanism to aggregate neighborhood information. However, we observed that message passing largely slows down the convergence of GCNs during training, especially for large-scale recommender systems, which hinders their wide adoption. LightGCN makes an early attempt to simplify GCNs for collaborative filtering by omitting feature transformations and nonlinear activations. In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation. Instead of explicit message passing, UltraGCN resorts to directly approximate the limit of infinite-layer graph convolutions via a constraint loss. Meanwhile, UltraGCN allows for more appropriate edge weight assignments and flexible adjustment of the relative importances among different types of relationships. This finally yields a simple yet effective UltraGCN model, which is easy to implement and efficient to train. Experimental results on four benchmark datasets show that UltraGCN not only outperforms the state-of-the-art GCN models but also achieves more than 10x speedup over LightGCN. Our source code will be available at https://reczoo.github.io/UltraGCN.

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Authors (6)
  1. Kelong Mao (23 papers)
  2. Jieming Zhu (68 papers)
  3. Xi Xiao (82 papers)
  4. Biao Lu (4 papers)
  5. Zhaowei Wang (36 papers)
  6. Xiuqiang He (97 papers)
Citations (267)
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Summary

An Analytical Overview of "UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation"

The paper "UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation" presents a novel approach to improving the efficiency and efficacy of Graph Convolutional Networks (GCNs) in the field of recommendation systems. By critically analyzing and addressing the limitations inherent in conventional GCN-based models, the authors introduce an ultra-simplified model, dubbed UltraGCN, designed to overcome the computational burdens associated with traditional GCNs while maintaining or enhancing performance metrics.

Simplification and Efficiency of UltraGCN

A primary innovation of UltraGCN is its completely reformulated approach to the conventional message-passing mechanism that characterizes GCNs. Traditional GCNs are known for utilizing complex message passing to aggregate information across nodes, which, while powerful, is computationally intensive, particularly for large graphs as encountered in industrial scale recommender systems. The UltraGCN model circumvents explicit message-passing by approximating the limit of infinite-layer graph convolution and leveraging a constraint loss to maintain model integrity. This approach not only addresses the over-smoothing problem notable in deep GCNs but also simplifies the training process significantly.

Structural Simplicity and Practical Applications

The UltraGCN model's core strength lies in its effective use of constraint losses over explicit message-passing. This simplification allows for efficient computation while reducing training epochs significantly – achieving more than 10x speedup over previously efficient models such as LightGCN. The empirical results demonstrate substantial improvements in key metrics: up to 76.6% enhancement in NDCG@20 on benchmarks like the Amazon-Books dataset. Such performance signifies practical utility for large-scale systems where computational resources and efficiency are critical concerns.

Implications and Future Directions

From a theoretical standpoint, UltraGCN challenges the necessity of explicit message-passing in modern graph-based recommendation models. It shifts the paradigm towards constraint-based optimization, offering insights into how edge weight assignments can be inherently more intuitive and less biased in diverse data contexts. Practically, the work underscores potential avenues for integrating the model with existing frameworks and applications, such as incorporating external knowledge graphs, enhancing scalability, and refining industrial recommendation systems with minimal computational overhead.

UltraGCN's design also suggests promising avenues for future research to explore simplification methods for other GNN architectures beyond recommendation tasks. There is potential to extend this constraint-based approach into heterogeneous graph settings and multi-relational domains, thereby broadening its applicability across various complex data environments.

Conclusion

UltraGCN stands as an important contribution to the domain of recommender systems, presenting a model that balances simplicity with high performance. This ultra-simplified approach, by advancing both the efficiency and capability of GCN-based recommendations, provides a valuable framework and benchmark for future innovation in graph-based machine learning techniques. As industry and academic focus on efficient, scalable solutions continues to grow, UltraGCN represents a forward-thinking step in aligning computational feasibility with advanced algorithmic design.

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