Graph Neural Networks for Social Recommendation (1902.07243v2)

Abstract: In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at \url{https://github.com/wenqifan03/GraphRec-WWW19}

• The paper presents the GraphRec framework that integrates opinion-aware user interactions and social ties via GNNs.
• It employs dual aggregation methods and attention mechanisms to fuse heterogeneous user and item data for enhanced rating predictions.
• Experimental evaluations on Ciao and Epinions reveal improved RMSE and MAE compared to conventional and neural network-based models.

Graph Neural Networks for Social Recommendation

Introduction

"Graph Neural Networks for Social Recommendation" introduces a novel framework named GraphRec, which utilizes the power of Graph Neural Networks (GNNs) to improve social recommendation systems by simultaneously considering user-item interactions, opinion embeddings, and social ties of varying strengths. By capturing these elements in a unified model, the authors aim to address common challenges in social recommendation systems, such as integrating information from heterogeneous sources (user-user and user-item graphs) and handling the intrinsic variation in social tie strengths.

Methodology

GraphRec Framework

GraphRec introduces a principled GNN-based framework to model the social recommendation task comprehensively. The primary components of the GraphRec system are divided into three sections: user modeling, item modeling, and rating prediction.

1. User Modeling: Two types of aggregations are employed:
   • Item Aggregation: Captures user preferences based on interactions and ratings within the user-item graph. This process introduces the concept of "opinion-aware interactions," where an item's embedding is adjusted according to user opinion (ratings). An attention mechanism is used to weigh the importance of each interaction.
   • Social Aggregation: Integrates relationships from the user-user social graph, considering varying tie strengths through an attention mechanism. This accounts for the fact that stronger social ties are more reflective of similar preferences.
2. Item Modeling: Uses an equivalent process to model item factors by aggregating information from multiple users that interact with an item. This is similarly facilitated by "opinion-aware user representations" and is further enhanced by attention to individual interaction significance.
3. Rating Prediction: Combines user and item latent factors obtained from the above processes through a Multi-Layer Perceptron (MLP). The predicted user-item ratings are then optimized using standard loss functions (MAE, RMSE).

Experimental Evaluation

GraphRec is evaluated on two real-world datasets, Ciao and Epinions, demonstrating substantial improvements over baseline models, including traditional matrix factorization approaches (PMF, SoRec) and state-of-the-art neural network-based methods (NeuMF, DeepSoR). Key observations include:

• GraphRec's performance is enhanced by effectively incorporating opinion embeddings and differentiating social tie strengths.
• Attention mechanisms provide notable benefits by dynamically adjusting to the significance of interactions and ties.

Results and Discussions

GraphRec achieves superior results in terms of RMSE and MAE, indicating that the model's deeper integration of both user opinions and social networks indeed provides a richer understanding of user preferences:

• Role of Opinions: Including opinion embeddings (ratings as features) reveals their pivotal role in differentiating between items of various user preferences.
• Attention Mechanisms: These mechanisms significantly contribute by tuning the relevance of each component in aggregated information, reflecting in enhanced recommendation accuracy.

Conclusion

The presented GraphRec model represents an advancement in the use of GNNs for extracting and capitalizing on both structural and feature-based properties inherent in social recommendation tasks. The modular design allowing for easy inclusion of attention and embedded opinion considerations could inspire further exploration and integration of diverse user contexts.

Future Directions

Potential future work should examine expanding GraphRec to accommodate dynamic models where evolving graphs and time-varying interactions are inherently considered, thus capturing user behavior and preferences changes over time. Additionally, incorporating auxiliary information (e.g., user demographic data or temporal dynamics) could further improve recommendation precision and scalability.