Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction (1910.05552v2)

Abstract: Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The key of this task is to model feature interactions among different feature fields. Recently proposed deep learning based models follow a general paradigm: raw sparse input multi-filed features are first mapped into dense field embedding vectors, and then simply concatenated together to feed into deep neural networks (DNN) or other specifically designed networks to learn high-order feature interactions. However, the simple \emph{unstructured combination} of feature fields will inevitably limit the capability to model sophisticated interactions among different fields in a sufficiently flexible and explicit fashion. In this work, we propose to represent the multi-field features in a graph structure intuitively, where each node corresponds to a feature field and different fields can interact through edges. The task of modeling feature interactions can be thus converted to modeling node interactions on the corresponding graph. To this end, we design a novel model Feature Interaction Graph Neural Networks (Fi-GNN). Taking advantage of the strong representative power of graphs, our proposed model can not only model sophisticated feature interactions in a flexible and explicit fashion, but also provide good model explanations for CTR prediction. Experimental results on two real-world datasets show its superiority over the state-of-the-arts.

• The paper introduces Fi-GNN, a novel graph-based framework that represents multi-field features as nodes to capture complex interactions for CTR prediction.
• It employs an attention mechanism to dynamically weigh inter-feature relationships, outperforming traditional models like factorization machines and DeepFM.
• Experimental results on datasets such as Criteo and Avazu demonstrate significant improvements in AUC and log-loss, affirming its effectiveness in digital advertising.

An Analysis of Fi-GNN: Leveraging Graph Neural Networks for CTR Prediction

The paper "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction" presents a novel approach to address the complexities associated with click-through rate (CTR) prediction by proposing the use of graph neural networks (GNNs). CTR prediction is crucial in domains like online advertising and recommender systems, where accurately predicting user engagement with digital content can significantly influence digital marketing strategies and user experience optimization.

Key Contributions

The central premise of Fi-GNN is to represent multi-field features typical of CTR tasks in a graph structure where each feature field is represented as a node. This transformation allows for the application of graph neural networks to model sophisticated feature interactions more effectively than traditional deep learning methods that rely on simple concatenation of feature embeddings.

Edge-wise Interaction Modelling: The Fi-GNN approach inherently captures interactions among features using graph edges, providing a flexible and explicit mechanism for feature interaction modeling. This is a step forward from conventional deep learning models which often limit interaction capacities by processing concatenated features in a structured but less flexible manner.

Attention Mechanism: The paper employs an attention mechanism to learn the weights of edges in the graph. This component is essential for quantifying the importance of different feature interactions which, until now, has been relatively implicit in traditional models. By employing attention, Fi-GNN ensures that influential and critical feature interactions are prioritized in the model's learning process.

Experimental Evidence

Experimental results provided in the paper demonstrate Fi-GNN's superiority over state-of-the-art methods across two large real-world datasets: Criteo and Avazu. Fi-GNN consistently outperformed existing methods, including factorization machines and various deep learning models like DeepFM and xDeepFM, on both AUC and log-loss metrics, showcasing its efficacy in capturing high-order feature interactions.

Implications and Future Work

Practical Implications: By adopting a graph-based representation of input features, Fi-GNN enhances the model explainability and interpretability in CTR prediction tasks. This capability is important for digital marketers and system designers who require not just accurate predictions but also insights into why certain user behaviors are predicted—a consideration that feeds into more effective advertising strategies.

Theoretical Implications: The framework melds the expressive power of GNNs with the nuanced needs of CTR prediction, paving the way for further explorations of graph-based models in other recommendation systems and high-dimensional interaction problems.

Future Directions: Speculatively, future enhancements could involve the exploration of dynamic graph structures that capture temporal aspect of feature interactions, further leveraging multi-layer GNNs to capture deeper interactions, or hybrid models that combine benefits of other neural architectures with Fi-GNN.

In conclusion, the Fi-GNN framework represents a significant advancement in CTR prediction methodology. Its adoption of feature interaction modeling via GNNs introduces a potent tool for navigating the complex and convoluted landscape of user interactions, potentially setting a new standard in performance and interpretability for predictive models in information systems.