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}

Graph Neural Networks for Social Recommendation

The integration of social relationships into recommender systems has been an area of growing interest. The paper "Graph Neural Networks for Social Recommendation" addresses this integration by proposing a novel Graph Neural Network (GNN) framework termed GraphRec. The primary goal is to harness the full potential of GNNs to enhance social recommendation tasks by tackling key challenges inherent in the recommendation scenarios involving social networks.

Key Contributions

The paper makes several significant contributions to the field:

1. Novel Framework: It introduces GraphRec, a GNN-based framework that can model graph data inherent in social recommendations inherently.
2. Joint Interaction and Opinion Capture: The framework presents a systematic approach to jointly capture both user-item interactions and users' opinions, a critical aspect for improving recommendation accuracy.
3. Handling Heterogeneous Social Relations: GraphRec integrates an attention mechanism to account for varying strengths in social relations, allowing for a more robust user representation.
4. Empirical Validation: Extensive experimentation on two real-world datasets validates the effectiveness of GraphRec, consistently outperforming state-of-the-art baselines.

Methodology

Definitions and Notations

The paper begins by establishing necessary definitions and notations, identifying key entities such as the set of users $U$, set of items $V$, user-item rating matrix $\mathbf{R}$, and the user-user social graph $\mathrm{T}$. Embeddings for users ($\mathbf{p}_i$), items ($\mathbf{q}_j$), and opinions ($\mathbf{e}_r$) are introduced to facilitate the learning process.

Framework Overview

GraphRec's architecture is designed around three core components:

1. User Modeling: Focuses on deriving latent representations for users through two types of aggregations: item aggregation and social aggregation.
2. Item Modeling: Concentrates on learning item representations by aggregating users' opinions and interactions.
3. Rating Prediction: Combines the learned user and item representations to predict user ratings on items using a Multi-Layer Perceptron (MLP).

User Modeling

Item Aggregation: This component aims to capture user-item interactions along with the accompanying opinions. Through an opinion-aware interaction representation (using both item and opinion embeddings) and an attention mechanism, this step generates item-space user latent factors.

Social Aggregation: Social-space user latent factors are learned by considering users' neighbors in the social graph, using an attention mechanism to account for different tie strengths.

Finally, user latent factors are synthesized by combining the item-space and social-space latent factors using an MLP.

Item Modeling

User Aggregation: Similar to item aggregation, this step learns item latent factors by aggregating users' interactions and opinions. An attention mechanism is employed to differentiate the significance of each user interaction.

Rating Prediction

The final step in GraphRec is predicting the ratings by combining user and item latent factors through an MLP. The objective function is formulated to minimize the predictive error (Mean Absolute Error and Root Mean Square Error) using the RMSprop optimizer.

Experimental Results

The experimental evaluation on the Ciao and Epinions datasets demonstrates the superiority of GraphRec over several baseline models:

• Traditional Models: Such as PMF and SoRec.
• Social Recommender Systems: Including SoReg, SocialMF, and TrustMF.
• Neural Network Models: Like NeuMF, DeepSoR, and GCMC+SN.

GraphRec consistently achieved lower MAE and RMSE scores across different training-test splits, underscoring its effectiveness in leveraging both social and opinion information.

Implications and Future Work

The research presents substantial theoretical and practical implications:

• Theoretical: It advances the understanding of integrating heterogeneous information from social and interaction data into GNN frameworks.
• Practical: The proposed framework can be applied to various real-world recommender systems, enhancing their accuracy by incorporating multifaceted social data.

Future developments could explore the integration of additional side information such as user and item attributes and dynamic graph structures. This exploration could further improve the robustness and applicability of the GNN-based social recommendation systems.

Conclusion

"Graph Neural Networks for Social Recommendation" presents a well-architected framework addressing critical challenges in social recommendation systems. By leveraging the advanced capabilities of GNNs and incorporating detailed user interactions and social relationships, GraphRec demonstrates superior performance, marking a significant contribution to the domain of social recommender systems.