Neural Graph Collaborative Filtering (1905.08108v2)

Abstract: Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions -- more specifically the bipartite graph structure -- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering.

Neural Graph Collaborative Filtering: Integrating High-Order Connectivity for Enhanced Recommender Systems

The paper "Neural Graph Collaborative Filtering" (NGCF) proposes a novel approach to embedding representation in collaborative filtering by leveraging the high-order connectivity inherent in the user-item interaction graph. Moving beyond traditional matrix factorization and recent neural methods, NGCF harnesses the bipartite graph structure of user-item interactions to explicitly encode collaborative signals during the embedding process, aiming to improve the quality of recommendations.

Methodological Advances

NGCF introduces a specialized neural network framework that encapsulates high-order connectivity through the propagation of embeddings on the user-item interaction graph. The main contributions of this framework are threefold:

1. Embedding Propagation: NGCF develops a mechanism whereby user and item embeddings propagate through the interaction graph, aggregating information from their neighbors. This propagation follows the principles of graph neural networks, ensuring that each node can assimilate information from multi-hop neighbors.
2. Explicit Encoding of Collaborative Signal: By explicitly incorporating the paths in the graph into the embedding process, NGCF ensures that user and item embeddings reflect the collaborative filtering effect more effectively. This differs from traditional methods where embeddings are often derived solely from user/item IDs and attribute features without explicitly involving the interaction graph.
3. Empirical Validation: Comprehensive experiments across three substantial public datasets (Gowalla, Yelp2018, and Amazon-Book) demonstrate NGCF's superiority over state-of-the-art models, including HOP-Rec and Collaborative Memory Network. These experiments underscore the efficacy of embedding propagation and the positive impact of capturing high-order interactions on recommendation performance.

Experimental Results

The empirical results indicate a significant performance enhancement. For instance, NGCF achieved recall and ndcg improvements of 11.68% and 8.64% on the Gowalla dataset, respectively. On the Yelp2018 dataset, the improvements reached 11.97% and 11.29%, respectively, while on the Amazon-Book dataset, the gains were 9.61% and 12.50%. These improvements underscore NGCF's ability to leverage high-order connectivity to robustly capture user preferences.

Practical and Theoretical Implications

Practically, NGCF's approach can be pivotal for platforms requiring personalized recommendations, such as e-commerce sites and streaming services. The method’s ability to exploit multi-hop neighbor information means that it can feasibly handle sparse datasets where user interactions are limited, thus potentially reducing the cold-start problem.

Theoretically, NGCF opens new research avenues in collaborative filtering by demonstrating the substantial benefits of integrating graph structure directly into the embedding process. This work aligns with an emerging trend of employing graph neural networks to model user-item interactions, suggesting future explorations into more sophisticated message-passing schemes and hybrid models incorporating other forms of user and item relational data.

Future Directions

Future research may explore several extensions of NGCF:

• Attention Mechanisms: Incorporating attention mechanisms within the propagation layers could allow the model to learn dynamic weights for different neighbors, enhancing its ability to discern influential interactions.
• Adversarial Learning: Implementing adversarial techniques could improve the robustness of the embeddings against perturbations, fostering more resilient recommendation systems.
• Integration with Knowledge Graphs: Combining user-item interaction graphs with item knowledge graphs could yield more semantically enriched recommendations, fostering a deeper understanding of user preferences.
• Scalability: While NGCF demonstrates strong performance on benchmark datasets, further work focusing on scalability and computational efficiency could facilitate deployment in large-scale, real-time recommendation systems.

Conclusion

"Neural Graph Collaborative Filtering" significantly advances the field of recommender systems by integrating high-order user-item connectivities into the core of the embedding process. The empirical evidence supports the methodological innovations, showing meaningful improvements over existing state-of-the-art methods. By explicitly encoding collaborative signals within a graph neural network framework, NGCF sets a new precedent for the development of sophisticated, high-performing recommendation algorithms.