ContextGNN: Beyond Two-Tower Recommendation Systems (2411.19513v1)

Abstract: Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average.

• The paper presents ContextGNN, a hybrid model that fuses pair-wise dynamics with two-tower embeddings to markedly improve recommendation accuracy.
• It leverages a graph neural network to capture localized user-item interactions while mitigating the quadratic complexity of traditional pair-wise methods.
• Empirical evaluations across diverse datasets reveal a consistent 20% improvement in mean average precision over established recommendation models.

ContextGNN: Beyond Two-Tower Recommendation Systems

The paper "ContextGNN: Beyond Two-Tower Recommendation Systems" presents an innovative approach to enhancing recommendation systems by introducing a hybrid model that combines pair-wise and two-tower representations. This model addresses several critical weaknesses inherent in traditional two-tower architectures, primarily their inability to account for pair-specific relationships between users and items. The authors propose a novel Context-based Graph Neural Network (ContextGNN) which effectively integrates the insights from both pair-wise and traditional user-item ranking models to improve recommendation accuracy.

Overview of the Approach

ContextGNN tackles the fundamental limitations of two-tower recommendation architectures by leveraging the advantages of both pair-wise and two-tower representations within a single framework. Traditional two-tower models are noted for their efficient user-item ranking via embeddings; however, they fall short in contextualizing individual user-item interactions. Conversely, pair-wise models, although adept at capturing relationship nuances, often struggle with scaling due to quadratic complexity concerns.

To balance these paradigms, ContextGNN utilizes a Graph Neural Network (GNN) to harness user-item interaction data effectively. The architecture employs pair-wise representation techniques focused on local subgraphs while utilizing two-tower models for items beyond direct user interactions. This dual approach allows for leveraging localized, detailed interaction data and broader item exploration dynamics.

A substantial innovation within ContextGNN is its ability to predict how to merge pair-wise and two-tower scores into a unified ranking. This allows the system to adaptively weight familiar and exploratory items based on user profiles, thereby catering to more nuanced recommendation tasks.

Empirical Evaluation

The authors conducted comprehensive experiments on several datasets from the Relational Deep Learning domain, including rel-amazon, rel-hm, and rel-stack, each embodying unique user-item interaction characteristics. The experimental results demonstrate that ContextGNN significantly outperforms existing models, such as NBFNet and traditional GNN-based systems, with an average performance improvement of 20% over the best existing pair-wise baseline.

Numerical Results

The empirical results reveal that ContextGNN achieves substantial performance gains on recommendation tasks, improving the mean average precision (MAP) metric across varied datasets. For instance, on the rel-amazon dataset, the proposed model not only consistently surpassed baseline methods but also improved the average performance over a ten-task benchmark, which underscores its versatility and effectiveness in diverse recommendation scenarios.

Theoretical and Practical Implications

The introduction of ContextGNN provides compelling insights into the development of more refined recommendation systems, presenting a model that does not solely rely on paired or two-tower embedding frameworks but combines them to pursue adaptive recommendation strategies. Theoretically, this model advances the graph neural networks' application in recommendation scenarios by expanding the depth of captured relationships between entities.

Practically, ContextGNN's design principles can significantly influence systems handling large-scale, complex recommendation tasks, where user behavior cannot be succinctly modeled using one-dimensional frameworks. With the promising results highlighted, the implementation of such a model could substantially uplift recommendation quality in e-commerce platforms, streaming services, and other domains where personalized content delivery is crucial.

Future Directions

The paper opens several avenues for future research. Investigating the model's performance in handling cold-start scenarios and newly emerging items can offer further enhancements. Additionally, extending the framework to incorporate self-supervised learning mechanisms or refining the model for more lightweight, scalable deployments could present potential areas of exploration.

In conclusion, ContextGNN represents an important step forward in the evolution of recommendation systems. By harmonizing pair-wise and tower representations within graph neural networks, it provides an adaptable and robust approach to tackling the multifaceted challenges of modern recommendation tasks. The rich insights and effective methodology presented render it a critical contribution worthy of consideration in both academic and practical advances in AI-driven recommendation systems.